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per page: <a href=/list/eess.SY/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> <dl id='articles'> <h3>New submissions (showing 17 of 17 entries)</h3> <dt> <a name='item1'>[1]</a> <a href ="/abs/2503.13552" title="Abstract" id="2503.13552"> arXiv:2503.13552 </a> [<a href="/pdf/2503.13552" title="Download PDF" id="pdf-2503.13552" aria-labelledby="pdf-2503.13552">pdf</a>, <a href="/format/2503.13552" title="Other formats" id="oth-2503.13552" aria-labelledby="oth-2503.13552">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fast data augmentation for battery degradation prediction </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+W">Weihan Li</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Samsukha,+H">Harshvardhan Samsukha</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=van+Vlijmen,+B">Bruis van Vlijmen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yan,+L">Lisen Yan</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Greenbank,+S">Samuel Greenbank</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Onori,+S">Simona Onori</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Viswanathan,+V">Venkat Viswanathan</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data. However, generating such data, whether in the laboratory or the field, is time- and resource-intensive. Here, we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization, aiming to augment the datasets used by data-driven models for degradation prediction. We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications. These datasets encompass various chemistries and realistic conditions, including cell-to-cell variations, measurement noise, varying charge-discharge conditions, and capacity recovery. Our results show that it is possible to reduce cell-testing efforts by at least 50% by substituting synthetic data into an existing dataset. This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments. </p> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2503.13583" title="Abstract" id="2503.13583"> arXiv:2503.13583 </a> [<a href="/pdf/2503.13583" title="Download PDF" id="pdf-2503.13583" aria-labelledby="pdf-2503.13583">pdf</a>, <a href="https://arxiv.org/html/2503.13583v1" title="View HTML" id="html-2503.13583" aria-labelledby="html-2503.13583" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13583" title="Other formats" id="oth-2503.13583" aria-labelledby="oth-2503.13583">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Stability results for MIMO LTI systems via Scaled Relative Graphs </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Baron-Prada,+E">Eder Baron-Prada</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Anta,+A">Adolfo Anta</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Padoan,+A">Alberto Padoan</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=D%C3%B6rfler,+F">Florian D枚rfler</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To be submitted to CDC 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> This paper proposes a new approach for stability analysis of multi-input, multi-output (MIMO) feedback systems through Scaled Relative Graphs (SRGs). Unlike traditional methods, such as the Generalized Nyquist Criterion (GNC), which relies on a coupled analysis that requires the multiplication of models, our approach enables the evaluation of system stability in a decoupled fashion and provides an intuitive, visual representation of system behavior. Our results provide conditions for certifying the stability of feedback MIMO Linear Time-Invariant (LTI) systems. </p> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2503.13688" title="Abstract" id="2503.13688"> arXiv:2503.13688 </a> [<a href="/pdf/2503.13688" title="Download PDF" id="pdf-2503.13688" aria-labelledby="pdf-2503.13688">pdf</a>, <a href="https://arxiv.org/html/2503.13688v1" title="View HTML" id="html-2503.13688" aria-labelledby="html-2503.13688" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13688" title="Other formats" id="oth-2503.13688" aria-labelledby="oth-2503.13688">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Cooperative Deterministic Learning-Based Formation Control for a Group of Nonlinear Mechanical Systems Under Complete Uncertainty </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Norouzi,+M">Maryam Norouzi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhou,+M">Mingxi Zhou</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yuan,+C">Chengzhi Yuan</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 6 figures, Conference </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> In this work we address the formation control problem for a group of nonlinear mechanical systems with complete uncertain dynamics under a virtual leader-following framework. We propose a novel cooperative deterministic learning-based adaptive formation control algorithm. This algorithm is designed by utilizing artificial neural networks to simultaneously achieve formation tracking control and locally-accurate identification/learning of the nonlinear uncertain dynamics of the considered group of mechanical systems. To demonstrate the practicality and verify the effectiveness of the proposed results, numerical simulations have been conducted. </p> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2503.13754" title="Abstract" id="2503.13754"> arXiv:2503.13754 </a> [<a href="/pdf/2503.13754" title="Download PDF" id="pdf-2503.13754" aria-labelledby="pdf-2503.13754">pdf</a>, <a href="https://arxiv.org/html/2503.13754v1" title="View HTML" id="html-2503.13754" aria-labelledby="html-2503.13754" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13754" title="Other formats" id="oth-2503.13754" aria-labelledby="oth-2503.13754">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> From Autonomous Agents to Integrated Systems, A New Paradigm: Orchestrated Distributed Intelligence </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Tallam,+K">Krti Tallam</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> The rapid evolution of artificial intelligence (AI) has ushered in a new era of integrated systems that merge computational prowess with human decision-making. In this paper, we introduce the concept of \textbf{Orchestrated Distributed Intelligence (ODI)}, a novel paradigm that reconceptualizes AI not as isolated autonomous agents, but as cohesive, orchestrated networks that work in tandem with human expertise. ODI leverages advanced orchestration layers, multi-loop feedback mechanisms, and a high cognitive density framework to transform static, record-keeping systems into dynamic, action-oriented environments. Through a comprehensive review of multi-agent system literature, recent technological advances, and practical insights from industry forums, we argue that the future of AI lies in integrating distributed intelligence within human-centric workflows. This approach not only enhances operational efficiency and strategic agility but also addresses challenges related to scalability, transparency, and ethical decision-making. Our work outlines key theoretical implications and presents a practical roadmap for future research and enterprise innovation, aiming to pave the way for responsible and adaptive AI systems that drive sustainable innovation in human organizations. </p> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2503.13788" title="Abstract" id="2503.13788"> arXiv:2503.13788 </a> [<a href="/pdf/2503.13788" title="Download PDF" id="pdf-2503.13788" aria-labelledby="pdf-2503.13788">pdf</a>, <a href="https://arxiv.org/html/2503.13788v1" title="View HTML" id="html-2503.13788" aria-labelledby="html-2503.13788" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13788" title="Other formats" id="oth-2503.13788" aria-labelledby="oth-2503.13788">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Geometry of the Feasible Output Regions of Grid-Interfacing Inverters with Current Limits </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Streitmatter,+L">Lauren Streitmatter</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Joswig-Jones,+T">Trager Joswig-Jones</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+B">Baosen Zhang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> Many resources in the grid connect to power grids via programmable grid-interfacing inverters that can provide grid services and offer greater control flexibility and faster response times compared to synchronous generators. However, the current through the inverter needs to be limited to protect the semiconductor components. Existing controllers are designed using somewhat ad hoc methods, for example, by adding current limiters to preexisting control loops, which can lead to stability issues or overly conservative operations. <br>In this paper, we study the geometry of the feasible output region of a current-limited inverter. We show that under a commonly used model, the feasible region is convex. We provide an explicit characterization of this region, which allows us to efficiently find the optimal operating points of the inverter. We demonstrate how knowing the feasible set and its convexity allows us to design safe controllers such that the transient trajectories always remain within the current magnitude limit, whereas standard droop controllers can lead to violations. </p> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2503.13958" title="Abstract" id="2503.13958"> arXiv:2503.13958 </a> [<a href="/pdf/2503.13958" title="Download PDF" id="pdf-2503.13958" aria-labelledby="pdf-2503.13958">pdf</a>, <a href="https://arxiv.org/html/2503.13958v1" title="View HTML" id="html-2503.13958" aria-labelledby="html-2503.13958" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13958" title="Other formats" id="oth-2503.13958" aria-labelledby="oth-2503.13958">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> What was Said, What was not Said </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Jahanian,+H">Hamid Jahanian</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> In the process industry, the configuration of Safety Instrumented Systems (SIS) must comply with a defined set of safety requirements, typically documented in the Safety Requirements Specification (SRS). The functional safety standard IEC 61511 outlines the necessary content and quality criteria for the SRS. However, developing an effective SRS can be challenging. This article examines some of these challenges and proposes good practices to address them. It discusses SRS ownership, "staged" development of SRS, and the classification and traceability of requirements. Additionally, it explores the issue of untold "negative" requirements and suggests exploratory "inspection" of SIS Application Programs (APs) as a potential remedy. </p> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2503.13973" title="Abstract" id="2503.13973"> arXiv:2503.13973 </a> [<a href="/pdf/2503.13973" title="Download PDF" id="pdf-2503.13973" aria-labelledby="pdf-2503.13973">pdf</a>, <a href="https://arxiv.org/html/2503.13973v1" title="View HTML" id="html-2503.13973" aria-labelledby="html-2503.13973" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13973" title="Other formats" id="oth-2503.13973" aria-labelledby="oth-2503.13973">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Identification of non-causal systems with random switching modes (Extended Version) </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+Y">Yanxin Zhang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yu,+C">Chengpu Yu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Fabiani,+F">Filippo Fabiani</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> We consider the identification of non-causal systems with random switching modes (NCSRSM), a class of models essential for describing typical power load management and department store inventory dynamics. The simultaneous identification of causal-andanticausal subsystems, along with the presence of random switching sequences, however, make the overall identification problem particularly challenging. To this end, we develop an expectation-maximization (EM) based system identification technique, where the E-step proposes a modified Kalman filter (KF) to estimate the states and switching sequences of causal-and-anticausal subsystems, while the M-step consists in a switching least-squares algorithm to estimate the parameters of individual subsystems. We establish the main convergence features of the proposed identification procedure, also providing bounds on the parameter estimation errors under mild conditions. Finally, the effectiveness of our identification method is validated through two numerical simulations. </p> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2503.13996" title="Abstract" id="2503.13996"> arXiv:2503.13996 </a> [<a href="/pdf/2503.13996" title="Download PDF" id="pdf-2503.13996" aria-labelledby="pdf-2503.13996">pdf</a>, <a href="https://arxiv.org/html/2503.13996v1" title="View HTML" id="html-2503.13996" aria-labelledby="html-2503.13996" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13996" title="Other formats" id="oth-2503.13996" aria-labelledby="oth-2503.13996">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Robust Safety Critical Control Under Multiple State and Input Constraints: Volume Control Barrier Function Method </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Dong,+J">Jinyang Dong</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wu,+S">Shizhen Wu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Liu,+R">Rui Liu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Liang,+X">Xiao Liang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Lu,+B">Biao Lu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Fang,+Y">Yongchun Fang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Robotics (cs.RO) </div> <p class='mathjax'> In this paper, the safety-critical control problem for uncertain systems under multiple control barrier function (CBF) constraints and input constraints is investigated. A novel framework is proposed to generate a safety filter that minimizes changes to reference inputs when safety risks arise, ensuring a balance between safety and performance. A nonlinear disturbance observer (DOB) based on the robust integral of the sign of the error (RISE) is used to estimate system uncertainties, ensuring that the estimation error converges to zero exponentially. This error bound is integrated into the safety-critical controller to reduce conservativeness while ensuring safety. To further address the challenges arising from multiple CBF and input constraints, a novel Volume CBF (VCBF) is proposed by analyzing the feasible space of the quadratic programming (QP) problem. % ensuring solution feasibility by keeping the volume as a positive value. To ensure that the feasible space does not vanish under disturbances, a DOB-VCBF-based method is introduced, ensuring system safety while maintaining the feasibility of the resulting QP. Subsequently, several groups of simulation and experimental results are provided to validate the effectiveness of the proposed controller. </p> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2503.14049" title="Abstract" id="2503.14049"> arXiv:2503.14049 </a> [<a href="/pdf/2503.14049" title="Download PDF" id="pdf-2503.14049" aria-labelledby="pdf-2503.14049">pdf</a>, <a href="https://arxiv.org/html/2503.14049v1" title="View HTML" id="html-2503.14049" aria-labelledby="html-2503.14049" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14049" title="Other formats" id="oth-2503.14049" aria-labelledby="oth-2503.14049">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Modular Edge Device Network for Surgery Digitalization </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Schorp,+V">Vincent Schorp</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Giraud,+F">Fr茅d茅ric Giraud</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Parg%C3%A4tzi,+G">Gianluca Parg盲tzi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=W%C3%A4spe,+M">Michael W盲spe</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=von+Ritter-Zahony,+L">Lorenzo von Ritter-Zahony</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wegmann,+M">Marcel Wegmann</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Henao,+J+G">John Garcia Henao</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Cachin,+D">Dominique Cachin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Caprara,+S">Sebastiano Caprara</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=F%C3%BCrnstahl,+P">Philipp F眉rnstahl</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Carrillo,+F">Fabio Carrillo</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Hardware Architecture (cs.AR); Human-Computer Interaction (cs.HC); Networking and Internet Architecture (cs.NI) </div> <p class='mathjax'> Future surgical care demands real-time, integrated data to drive informed decision-making and improve patient outcomes. The pressing need for seamless and efficient data capture in the OR motivates our development of a modular solution that bridges the gap between emerging machine learning techniques and interventional medicine. We introduce a network of edge devices, called Data Hubs (DHs), that interconnect diverse medical sensors, imaging systems, and robotic tools via optical fiber and a centralized network switch. Built on the NVIDIA Jetson Orin NX, each DH supports multiple interfaces (HDMI, USB-C, Ethernet) and encapsulates device-specific drivers within Docker containers using the Isaac ROS framework and ROS2. A centralized user interface enables straightforward configuration and real-time monitoring, while an Nvidia DGX computer provides state-of-the-art data processing and storage. We validate our approach through an ultrasound-based 3D anatomical reconstruction experiment that combines medical imaging, pose tracking, and RGB-D data acquisition. </p> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2503.14104" title="Abstract" id="2503.14104"> arXiv:2503.14104 </a> [<a href="/pdf/2503.14104" title="Download PDF" id="pdf-2503.14104" aria-labelledby="pdf-2503.14104">pdf</a>, <a href="https://arxiv.org/html/2503.14104v1" title="View HTML" id="html-2503.14104" aria-labelledby="html-2503.14104" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14104" title="Other formats" id="oth-2503.14104" aria-labelledby="oth-2503.14104">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sheaf-Theoretic Causal Emergence for Resilience Analysis in Distributed Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Krasnovsky,+A+A">Anatoly A. Krasnovsky</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Discrete Mathematics (cs.DM); Information Theory (cs.IT); Software Engineering (cs.SE) </div> <p class='mathjax'> Distributed systems often exhibit emergent behaviors that impact their resilience (Franz-Kaiser et al., 2020; Adilson E. Motter, 2002; Jianxi Gao, 2016). This paper presents a theoretical framework combining attributed graph models, flow-on-graph simulation, and sheaf-theoretic causal emergence analysis to evaluate system resilience. We model a distributed system as a graph with attributes (capturing component state and connections) and use sheaf theory to formalize how local interactions compose into global states. A flow simulation on this graph propagates functional loads and failures. To assess resilience, we apply the concept of causal emergence, quantifying whether macro-level dynamics (coarse-grained groupings) exhibit stronger causal efficacy (via effective information) than micro-level dynamics. The novelty lies in uniting sheaf-based formalization with causal metrics to identify emergent resilient structures. We discuss limitless potential applications (illustrated by microservices, neural networks, and power grids) and outline future steps toward implementing this framework (Lake et al., 2015). </p> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/2503.14119" title="Abstract" id="2503.14119"> arXiv:2503.14119 </a> [<a href="/pdf/2503.14119" title="Download PDF" id="pdf-2503.14119" aria-labelledby="pdf-2503.14119">pdf</a>, <a href="https://arxiv.org/html/2503.14119v1" title="View HTML" id="html-2503.14119" aria-labelledby="html-2503.14119" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14119" title="Other formats" id="oth-2503.14119" aria-labelledby="oth-2503.14119">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Decentralized Continuification Control of Multi-Agent Systems via Distributed Density Estimation </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Di+Lorenzo,+B">Beniamino Di Lorenzo</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Maffettone,+G+C">Gian Carlo Maffettone</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=di+Bernardo,+M">Mario di Bernardo</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> This paper introduces a novel decentralized implementation of a continuification-based strategy to control the density of large-scale multi-agent systems on the unit circle. While continuification methods effectively address micro-to-macro control problems by reformulating ordinary/stochastic differential equations (ODEs/SDEs) agent-based models into more tractable partial differential equations (PDEs), they traditionally require centralized knowledge of macroscopic state observables. We overcome this limitation by developing a distributed density estimation framework that combines kernel density estimation with PI consensus dynamics. Our approach enables agents to compute local density estimates and derive local control actions using only information from neighboring agents in a communication network. Numerical validations across multiple scenarios - including regulation, tracking, and time-varying communication topologies - confirm the effectiveness of the proposed approach. They also convincingly demonstrate that our decentralized implementation achieves performance comparable to centralized approaches while enhancing reliability and practical applicability. </p> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2503.14222" title="Abstract" id="2503.14222"> arXiv:2503.14222 </a> [<a href="/pdf/2503.14222" title="Download PDF" id="pdf-2503.14222" aria-labelledby="pdf-2503.14222">pdf</a>, <a href="https://arxiv.org/html/2503.14222v1" title="View HTML" id="html-2503.14222" aria-labelledby="html-2503.14222" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14222" title="Other formats" id="oth-2503.14222" aria-labelledby="oth-2503.14222">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Stacked-Residual PINN for State Reconstruction of Hyperbolic Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Eshkofti,+K">Katayoun Eshkofti</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Barreau,+M">Matthieu Barreau</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> In a more connected world, modeling multi-agent systems with hyperbolic partial differential equations (PDEs) offers a potential solution to the curse of dimensionality. However, classical control tools need adaptation for these complex systems. Physics-informed neural networks (PINNs) provide a powerful framework to fix this issue by inferring solutions to PDEs by embedding governing equations into the neural network. A major limitation of original PINNs is their inability to capture steep gradients and discontinuities in hyperbolic PDEs. This paper proposes a stacked residual PINN method enhanced with a vanishing viscosity mechanism. Initially, a basic PINN with a small viscosity coefficient provides a stable, low-fidelity solution. Residual correction blocks with learnable scaling parameters then iteratively refine this solution, progressively decreasing the viscosity coefficient to transition from parabolic to hyperbolic PDEs. Applying this method to traffic state reconstruction improved results by an order of magnitude in relative $\mathcal{L}^2$ error, demonstrating its potential to accurately estimate solutions where original PINNs struggle with instability and low fidelity. </p> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2503.14309" title="Abstract" id="2503.14309"> arXiv:2503.14309 </a> [<a href="/pdf/2503.14309" title="Download PDF" id="pdf-2503.14309" aria-labelledby="pdf-2503.14309">pdf</a>, <a href="/format/2503.14309" title="Other formats" id="oth-2503.14309" aria-labelledby="oth-2503.14309">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> An Assessment of the UK Government Clean Energy Strategy for the Year 2030 </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Stephens,+A+D">Anthony D. Stephens</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Walwyn,+D+R">David R. Walwyn</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 7 figures, 3 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> In 2024, the UK Government made two striking announcements on its plans to decarbonise the energy system; it pledged GBP22 billion to establish carbon capture and storage hubs on Teesside and Merseyside and released the Clean Power 2030 Action Plan. This paper questions the validity of both plans, arguing that they do not take adequate account of the consequences of the highly variable nature of wind and solar generations. Using dynamic models of future UK electricity systems which are designed to take account of these variabilities, it is shown that the Clean Power 2030 Action Plan overestimates the ability of wind and solar generations to decarbonise the electricity system as they increase in size relative to the demand of the electricity system. More importantly, the dynamic models show that most of the achievable decarbonization is the result of increasing wind generation from the current level of around 10 GW to around 20 GW. Increasing wind generation to only 20 GW, rather than to 30 GW as proposed in the Action Plan, should halve the proposed cost, a saving of perhaps GBP 120 billion, with little disbenefit in terms of reduced decarbonization. Furthermore, the dynamic modelling shows that UK gas storage capacity of 7.5 winter days looks hopeless inadequate in comparison with the storage capacities deemed necessary by its continental neighbors. Concern is expressed that a consequence of the Climate Change Act of 2008 requiring the UK to meet arbitrary decarbonization targets is leading government advisors to propose several unproven and therefore highly risky technological solutions. </p> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2503.14372" title="Abstract" id="2503.14372"> arXiv:2503.14372 </a> [<a href="/pdf/2503.14372" title="Download PDF" id="pdf-2503.14372" aria-labelledby="pdf-2503.14372">pdf</a>, <a href="https://arxiv.org/html/2503.14372v1" title="View HTML" id="html-2503.14372" aria-labelledby="html-2503.14372" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14372" title="Other formats" id="oth-2503.14372" aria-labelledby="oth-2503.14372">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Online ResNet-Based Adaptive Control for Nonlinear Target Tracking </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Nino,+C+F">Cristian F. Nino</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Patil,+O+S">Omkar Sudhir Patil</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Eisman,+M+R">Marla R. Eisman</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Dixon,+W+E">Warren E. Dixon</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 6 pages, 2 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> This work introduces a generalized ResNet architecture for adaptive control of nonlinear systems with black box uncertainties. The approach overcomes limitations in existing methods by incorporating pre-activation shortcut connections and a zeroth layer block that accommodates different input-output dimensions. The developed Lyapunov-based adaptation law establishes semi-global exponential convergence to a neighborhood of the target state despite unknown dynamics and disturbances. Furthermore, the theoretical results are validated through a comparative simulation. </p> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2503.14379" title="Abstract" id="2503.14379"> arXiv:2503.14379 </a> [<a href="/pdf/2503.14379" title="Download PDF" id="pdf-2503.14379" aria-labelledby="pdf-2503.14379">pdf</a>, <a href="https://arxiv.org/html/2503.14379v1" title="View HTML" id="html-2503.14379" aria-labelledby="html-2503.14379" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14379" title="Other formats" id="oth-2503.14379" aria-labelledby="oth-2503.14379">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On the Standard Performance Criteria for Applied Control Design: PID, MPC or Machine Learning Controller? </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Sarhadi,+P">Pouria Sarhadi</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> The traditional control theory and its application to basic and complex systems have reached an advanced level of maturity. This includes aerial, marine, and ground vehicles, as well as robotics, chemical, transportation, and electrical systems widely used in our daily lives. The emerging era of data-driven methods, Large Language Models (LLMs), and AI-based controllers does not indicate a weakness in well-established control theory. Instead, it aims to reduce dependence on models and uncertainties, address increasingly complex systems, and potentially achieve decision-making capabilities comparable to human-level performance. This revolution integrates knowledge from computer science, machine learning, biology, and classical control, producing promising algorithms that are yet to demonstrate widespread real-world applicability. Despite the maturity of control theory and the presence of various performance criteria, there is still a lack of standardised metrics for testing, evaluation, Verification and Validation ($V\&V$) of algorithms. This gap can lead to algorithms that, while optimal in certain aspects, may fall short of practical implementation, sparking debates within the literature. For a controller to succeed in real-world applications, it must satisfy three key categories of performance metrics: tracking quality, control effort (energy consumption), and robustness. This paper rather takes an applied perspective, proposing and consolidating standard performance criteria for testing and analysing control systems, intended for researchers and students. The proposed framework ensures the post-design applicability of a black-box algorithm, aligning with modern data analysis and $V\&V$ perspectives to prevent resource allocation to systems with limited impact or imprecise claims. </p> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2503.14409" title="Abstract" id="2503.14409"> arXiv:2503.14409 </a> [<a href="/pdf/2503.14409" title="Download PDF" id="pdf-2503.14409" aria-labelledby="pdf-2503.14409">pdf</a>, <a href="https://arxiv.org/html/2503.14409v1" title="View HTML" id="html-2503.14409" aria-labelledby="html-2503.14409" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14409" title="Other formats" id="oth-2503.14409" aria-labelledby="oth-2503.14409">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Inference and Learning of Nonlinear LFR State-space Models </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Floren,+M">Merijn Floren</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=No%C3%ABl,+J">Jean-Philippe No毛l</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Swevers,+J">Jan Swevers</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> Estimating the parameters of nonlinear block-oriented state-space models from input-output data typically involves solving a highly non-convex optimization problem, making it susceptible to poor local minima and slow convergence. This paper presents a computationally efficient initialization method for fully parametrizing nonlinear linear fractional representation (NL-LFR) models using periodic data. The approach first infers the latent variables and then estimates the model parameters, yielding initial estimates that serve as a starting point for further nonlinear optimization. The proposed method shows robustness against poor local minima, and achieves a twofold error reduction compared to the state-of-the-art on a challenging benchmark dataset. </p> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2503.14418" title="Abstract" id="2503.14418"> arXiv:2503.14418 </a> [<a href="/pdf/2503.14418" title="Download PDF" id="pdf-2503.14418" aria-labelledby="pdf-2503.14418">pdf</a>, <a href="https://arxiv.org/html/2503.14418v1" title="View HTML" id="html-2503.14418" aria-labelledby="html-2503.14418" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14418" title="Other formats" id="oth-2503.14418" aria-labelledby="oth-2503.14418">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Decentralized RISE-based Control for Exponential Heterogeneous Multi-Agent Target Tracking of Second-Order Nonlinear Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Nino,+C+F">Cristian F. Nino</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Patil,+O+S">Omkar Sudhir Patil</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Edwards,+S+C">Sage C. Edwards</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Dixon,+W+E">Warren E. Dixon</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 6 pages, 1 figure </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> This work presents a decentralized implementation of a Robust Integral of the Sign of the Error (RISE) controller for multi-agent target tracking problems with exponential convergence guarantees. Previous RISE-based approaches for multi-agent systems required 2-hop communication, limiting practical applicability. New insights from a Lyapunov-based design-analysis approach are used to eliminate the need for multi-hop communication required in previous literature, while yielding exponential target tracking. The new insights include the development of a new P-function which is developed which works in tandem with the inclusion of the interaction matrix in the Lyapunov function. Nonsmooth Lyapunov-based stability analysis methods are used to yield semi-global exponential convergence to the target agent state despite the presence of bounded disturbances with bounded derivatives. The resulting outcome is a controller that achieves exponential target tracking with only local information exchange between neighboring agents. </p> </div> </dd> </dl> <dl id='articles'> <h3>Cross submissions (showing 15 of 15 entries)</h3> <dt> <a name='item18'>[18]</a> <a href ="/abs/2503.13474" title="Abstract" id="2503.13474"> arXiv:2503.13474 </a> (cross-list from eess.SP) [<a href="/pdf/2503.13474" title="Download PDF" id="pdf-2503.13474" aria-labelledby="pdf-2503.13474">pdf</a>, <a href="/format/2503.13474" title="Other formats" id="oth-2503.13474" aria-labelledby="oth-2503.13474">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ISLS: An IoT-Based Smart Lighting System for Improving Energy Conservation in Office Buildings </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Obioma,+P">Peace Obioma</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Agbodike,+O">Obinna Agbodike</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Chen,+J">Jenhui Chen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+L">Lei Wang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> With the Internet of Things (IoT) fostering seamless device-to-human and device-to-device interactions, the domain of intelligent lighting systems have evolved beyond simple occupancy and daylight sensing towards autonomous monitoring and control of power consumption and illuminance levels. To this regard, this paper proposes a new do-it-yourself (DIY) IoT-based method of smart lighting system featuring an illuminance control algorithm. The design involves the integration of occupancy and presence sensors alongside a communication module, to enable real-time wireless interaction and remote monitoring of the system parameters from any location through an end-user application. A constrained optimization problem was formulated to determine the optimal dimming vector for achieving target illuminance at minimal power consumption. The simplex algorithm was used to solve this problem, and the system's performance was validated through both MATLAB simulations and real-world prototype testing in an indoor office environment. The obtained experimental results demonstrate substantial power savings across multiple user occupancy scenarios, achieving reductions of approx. 80%, 48%, and 26% for one, two, and four user settings, respectively, in comparison to traditional basic lighting installation systems. </p> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/2503.13487" title="Abstract" id="2503.13487"> arXiv:2503.13487 </a> (cross-list from eess.SP) [<a href="/pdf/2503.13487" title="Download PDF" id="pdf-2503.13487" aria-labelledby="pdf-2503.13487">pdf</a>, <a href="https://arxiv.org/html/2503.13487v1" title="View HTML" id="html-2503.13487" aria-labelledby="html-2503.13487" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13487" title="Other formats" id="oth-2503.13487" aria-labelledby="oth-2503.13487">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Statistical Study of Sensor Data and Investigation of ML-based Calibration Algorithms for Inexpensive Sensor Modules: Experiments from Cape Point </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Barrett,+T">Travis Barrett</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Mishra,+A+K">Amit Kumar Mishra</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG); Systems and Control (eess.SY) </div> <p class='mathjax'> In this paper we present the statistical analysis of data from inexpensive sensors. We also present the performance of machine learning algorithms when used for automatic calibration such sensors. In this we have used low-cost Non-Dispersive Infrared CO$_2$ sensor placed at a co-located site at Cape Point, South Africa (maintained by Weather South Africa). The collected low-cost sensor data and site truth data are investigated and compared. We compare and investigate the performance of Random Forest Regression, Support Vector Regression, 1D Convolutional Neural Network and 1D-CNN Long Short-Term Memory Network models as a method for automatic calibration and the statistical properties of these model predictions. In addition, we also investigate the drift in performance of these algorithms with time. </p> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2503.13489" title="Abstract" id="2503.13489"> arXiv:2503.13489 </a> (cross-list from cs.AI) [<a href="/pdf/2503.13489" title="Download PDF" id="pdf-2503.13489" aria-labelledby="pdf-2503.13489">pdf</a>, <a href="https://arxiv.org/html/2503.13489v1" title="View HTML" id="html-2503.13489" aria-labelledby="html-2503.13489" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13489" title="Other formats" id="oth-2503.13489" aria-labelledby="oth-2503.13489">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> AI-driven control of bioelectric signalling for real-time topological reorganization of cells </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=de+Carvalho,+G+H">Gon莽alo Hora de Carvalho</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Artificial Intelligence (cs.AI)</span>; Systems and Control (eess.SY); Biological Physics (physics.bio-ph); Cell Behavior (q-bio.CB); Quantitative Methods (q-bio.QM) </div> <p class='mathjax'> Understanding and manipulating bioelectric signaling could present a new wave of progress in developmental biology, regenerative medicine, and synthetic biology. Bioelectric signals, defined as voltage gradients across cell membranes caused by ionic movements, play a role in regulating crucial processes including cellular differentiation, proliferation, apoptosis, and tissue morphogenesis. Recent studies demonstrate the ability to modulate these signals to achieve controlled tissue regeneration and morphological outcomes in organisms such as planaria and frogs. However, significant knowledge gaps remain, particularly in predicting and controlling the spatial and temporal dynamics of membrane potentials (V_mem), understanding their regulatory roles in tissue and organ development, and exploring their therapeutic potential in diseases. In this work we propose an experiment using Deep Reinforcement Learning (DRL) framework together with lab automation techniques for real-time manipulation of bioelectric signals to guide tissue regeneration and morphogenesis. The proposed framework should interact continuously with biological systems, adapting strategies based on direct biological feedback. Combining DRL with real-time measurement techniques -- such as optogenetics, voltage-sensitive dyes, fluorescent reporters, and advanced microscopy -- could provide a comprehensive platform for precise bioelectric control, leading to improved understanding of bioelectric mechanisms in morphogenesis, quantitative bioelectric models, identification of minimal experimental setups, and advancements in bioelectric modulation techniques relevant to regenerative medicine and cancer therapy. Ultimately, this research aims to utilize bioelectric signaling to develop new biomedical and bioengineering applications. </p> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2503.13674" title="Abstract" id="2503.13674"> arXiv:2503.13674 </a> (cross-list from cs.RO) [<a href="/pdf/2503.13674" title="Download PDF" id="pdf-2503.13674" aria-labelledby="pdf-2503.13674">pdf</a>, <a href="https://arxiv.org/html/2503.13674v1" title="View HTML" id="html-2503.13674" aria-labelledby="html-2503.13674" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13674" title="Other formats" id="oth-2503.13674" aria-labelledby="oth-2503.13674">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Transformable Modular Robots: A CPG-Based Approach to Independent and Collective Locomotion </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ding,+J">Jiayu Ding</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jakkula,+R">Rohit Jakkula</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xiao,+T">Tom Xiao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gan,+Z">Zhenyu Gan</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> Modular robotics enables the development of versatile and adaptive robotic systems with autonomous reconfiguration. This paper presents a modular robotic system in which each module has independent actuation, battery power, and control, allowing both individual mobility and coordinated locomotion. A hierarchical Central Pattern Generator (CPG) framework governs motion, with a low-level CPG controlling individual modules and a high-level CPG synchronizing inter-module coordination, enabling smooth transitions between independent and collective behaviors. To validate the system, we conduct simulations in MuJoCo and hardware experiments, evaluating locomotion across different configurations. We first analyze single-module motion, followed by two-module cooperative locomotion. Results demonstrate the effectiveness of the CPG-based control framework in achieving robust, flexible, and scalable locomotion. The proposed modular architecture has potential applications in search and rescue, environmental monitoring, and autonomous exploration, where adaptability and reconfigurability are essential. </p> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2503.13766" title="Abstract" id="2503.13766"> arXiv:2503.13766 </a> (cross-list from cs.LG) [<a href="/pdf/2503.13766" title="Download PDF" id="pdf-2503.13766" aria-labelledby="pdf-2503.13766">pdf</a>, <a href="https://arxiv.org/html/2503.13766v1" title="View HTML" id="html-2503.13766" aria-labelledby="html-2503.13766" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13766" title="Other formats" id="oth-2503.13766" aria-labelledby="oth-2503.13766">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A finite-sample bound for identifying partially observed linear switched systems from a single trajectory </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Racz,+D">Daniel Racz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Petreczky,+M">Mihaly Petreczky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Daroczy,+B">Balint Daroczy</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> We derive a finite-sample probabilistic bound on the parameter estimation error of a system identification algorithm for Linear Switched Systems. The algorithm estimates Markov parameters from a single trajectory and applies a variant of the Ho-Kalman algorithm to recover the system matrices. Our bound guarantees statistical consistency under the assumption that the true system exhibits quadratic stability. The proof leverages the theory of weakly dependent processes. To the best of our knowledge, this is the first finite-sample bound for this algorithm in the single-trajectory setting. </p> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2503.14053" title="Abstract" id="2503.14053"> arXiv:2503.14053 </a> (cross-list from cs.LG) [<a href="/pdf/2503.14053" title="Download PDF" id="pdf-2503.14053" aria-labelledby="pdf-2503.14053">pdf</a>, <a href="https://arxiv.org/html/2503.14053v1" title="View HTML" id="html-2503.14053" aria-labelledby="html-2503.14053" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14053" title="Other formats" id="oth-2503.14053" aria-labelledby="oth-2503.14053">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Rap,+J">Jake Rap</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Das,+A">Amritam Das</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Systems and Control (eess.SY) </div> <p class='mathjax'> Accurate traffic flow estimation and prediction are critical for the efficient management of transportation systems, particularly under increasing urbanization. Traditional methods relying on static sensors often suffer from limited spatial coverage, while probe vehicles provide richer, albeit sparse and irregular data. This work introduces ON-Traffic, a novel deep operator Network and a receding horizon learning-based framework tailored for online estimation of spatio-temporal traffic state along with quantified uncertainty by using measurements from moving probe vehicles and downstream boundary inputs. Our framework is evaluated in both numerical and simulation datasets, showcasing its ability to handle irregular, sparse input data, adapt to time-shifted scenarios, and provide well-calibrated uncertainty estimates. The results demonstrate that the model captures complex traffic phenomena, including shockwaves and congestion propagation, while maintaining robustness to noise and sensor dropout. These advancements present a significant step toward online, adaptive traffic management systems. </p> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2503.14055" title="Abstract" id="2503.14055"> arXiv:2503.14055 </a> (cross-list from math.OC) [<a href="/pdf/2503.14055" title="Download PDF" id="pdf-2503.14055" aria-labelledby="pdf-2503.14055">pdf</a>, <a href="https://arxiv.org/html/2503.14055v1" title="View HTML" id="html-2503.14055" aria-labelledby="html-2503.14055" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14055" title="Other formats" id="oth-2503.14055" aria-labelledby="oth-2503.14055">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Modular Distributed Nonconvex Learning with Error Feedback </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Carnevale,+G">Guido Carnevale</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Bastianello,+N">Nicola Bastianello</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Optimization and Control (math.OC)</span>; Machine Learning (cs.LG); Systems and Control (eess.SY) </div> <p class='mathjax'> In this paper, we design a novel distributed learning algorithm using stochastic compressed communications. In detail, we pursue a modular approach, merging ADMM and a gradient-based approach, benefiting from the robustness of the former and the computational efficiency of the latter. Additionally, we integrate a stochastic integral action (error feedback) enabling almost sure rejection of the compression error. We analyze the resulting method in nonconvex scenarios and guarantee almost sure asymptotic convergence to the set of stationary points of the problem. This result is obtained using system-theoretic tools based on stochastic timescale separation. We corroborate our findings with numerical simulations in nonconvex classification. </p> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2503.14083" title="Abstract" id="2503.14083"> arXiv:2503.14083 </a> (cross-list from eess.SP) [<a href="/pdf/2503.14083" title="Download PDF" id="pdf-2503.14083" aria-labelledby="pdf-2503.14083">pdf</a>, <a href="https://arxiv.org/html/2503.14083v1" title="View HTML" id="html-2503.14083" aria-labelledby="html-2503.14083" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14083" title="Other formats" id="oth-2503.14083" aria-labelledby="oth-2503.14083">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Modeling, Analysis, and Optimization of Cascaded Power Amplifiers </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Moryakova,+O">Oksana Moryakova</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Eriksson,+T">Thomas Eriksson</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Johansson,+H">H氓kan Johansson</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> This paper deals with modeling, analysis, and optimization of power amplifiers (PAs) placed in a cascaded structure, particularly the effect of cascaded nonlinearities is studied by showing potential ways to minimize the total nonlinearities. The nonlinear least-squares algorithm is proposed to optimize the PA parameters along with the input power level, and thereby minimize the total nonlinearities in the cascaded structure. The simulation results demonstrate that the performance of the optimized configurations for up to five PAs using the proposed framework can improve the linearity properties of the overall cascade. </p> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2503.14177" title="Abstract" id="2503.14177"> arXiv:2503.14177 </a> (cross-list from stat.ME) [<a href="/pdf/2503.14177" title="Download PDF" id="pdf-2503.14177" aria-labelledby="pdf-2503.14177">pdf</a>, <a href="https://arxiv.org/html/2503.14177v1" title="View HTML" id="html-2503.14177" aria-labelledby="html-2503.14177" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14177" title="Other formats" id="oth-2503.14177" aria-labelledby="oth-2503.14177">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Distributions and Direct Parametrization for Stable Stochastic State-Space Models </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Ahdab,+M+A">Mohamad Al Ahdab</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Tan,+Z">Zheng-Hua Tan</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Leth,+J">John Leth</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> We present a direct parametrization for continuous-time stochastic state-space models that ensures external stability via the stochastic bounded-real lemma. Our formulation facilitates the construction of probabilistic priors that enforce almost-sure stability which are suitable for sampling-based Bayesian inference methods. We validate our work with a simulation example and demonstrate its ability to yield stable predictions with uncertainty quantification. </p> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2503.14184" title="Abstract" id="2503.14184"> arXiv:2503.14184 </a> (cross-list from cs.RO) [<a href="/pdf/2503.14184" title="Download PDF" id="pdf-2503.14184" aria-labelledby="pdf-2503.14184">pdf</a>, <a href="https://arxiv.org/html/2503.14184v1" title="View HTML" id="html-2503.14184" aria-labelledby="html-2503.14184" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14184" title="Other formats" id="oth-2503.14184" aria-labelledby="oth-2503.14184">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ghotavadekar,+A">Atharva Ghotavadekar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nekov%C3%A1%C5%99,+F">Franti拧ek Nekov谩艡</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Saska,+M">Martin Saska</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Faigl,+J">Jan Faigl</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> IEEE Robotics and Automation Letters, vol. 10, no. 2, pp. 1249-1256, Feb. 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Artificial Intelligence (cs.AI); Systems and Control (eess.SY) </div> <p class='mathjax'> Agile trajectory planning can improve the efficiency of multi-rotor Uncrewed Aerial Vehicles (UAVs) in scenarios with combined task-oriented and kinematic trajectory planning, such as monitoring spatio-temporal phenomena or intercepting dynamic targets. Agile planning using existing non-linear model predictive control methods is limited by the number of planning steps as it becomes increasingly computationally demanding. That reduces the prediction horizon length, leading to a decrease in solution quality. Besides, the fixed time-step length limits the utilization of the available UAV dynamics in the target neighborhood. In this paper, we propose to address these limitations by introducing variable time steps and coupling them with the prediction horizon length. A simplified point-mass motion primitive is used to leverage the differential flatness of quadrotor dynamics and the generation of feasible trajectories in the flat output space. Based on the presented evaluation results and experimentally validated deployment, the proposed method increases the solution quality by enabling planning for long flight segments but allowing tightly sampled maneuvering. </p> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2503.14287" title="Abstract" id="2503.14287"> arXiv:2503.14287 </a> (cross-list from eess.SP) [<a href="/pdf/2503.14287" title="Download PDF" id="pdf-2503.14287" aria-labelledby="pdf-2503.14287">pdf</a>, <a href="https://arxiv.org/html/2503.14287v1" title="View HTML" id="html-2503.14287" aria-labelledby="html-2503.14287" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14287" title="Other formats" id="oth-2503.14287" aria-labelledby="oth-2503.14287">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Tosi,+E">Enrico Tosi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hu,+P">Panwei Hu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Ichkov,+A">Aleksandar Ichkov</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Petrova,+M">Marina Petrova</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Simi%C4%87,+L">Ljiljana Simi膰</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> Millimeter-wave (mm-wave) communications requirebeamforming and consequent precise beam alignmentbetween the gNodeB (gNB) and the user equipment (UE) toovercome high propagation losses. This beam alignment needs tobe constantly updated for different UE locations based on beamsweepingradio frequency measurements, leading to significantbeam management overhead. One potential solution involvesusing machine learning (ML) beam prediction algorithms thatleverage UE position information to select the serving beamwithout the overhead of beam sweeping. However, the highlysite-specific nature of mm-wave propagation means that MLmodels require training from scratch for each scenario, whichis inefficient in practice. In this paper, we propose a robustcross-environment transfer learning solution for location-aidedbeam prediction, whereby the ML model trained on a referencegNB is transferred to a target gNB by fine-tuning with a limiteddataset. Extensive simulation results based on ray-tracing in twourban environments show the effectiveness of our solution forboth inter- and intra-city model transfer. Our results show thatby training the model on a reference gNB and transferring themodel by fine-tuning with only 5% of the target gNB dataset,we can achieve 80% accuracy in predicting the best beamfor the target gNB. Importantly, our approach improves thepoor generalization accuracy of transferring the model to newenvironments without fine-tuning by around 75 percentage <a href="http://points.This" rel="external noopener nofollow" class="link-external link-http">this http URL</a> demonstrates that transfer learning enables high predictionaccuracy while reducing the computational and training datasetcollection burden of ML-based beam prediction, making itpractical for 5G-and-beyond deployments. </p> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2503.14297" title="Abstract" id="2503.14297"> arXiv:2503.14297 </a> (cross-list from cs.LG) [<a href="/pdf/2503.14297" title="Download PDF" id="pdf-2503.14297" aria-labelledby="pdf-2503.14297">pdf</a>, <a href="https://arxiv.org/html/2503.14297v1" title="View HTML" id="html-2503.14297" aria-labelledby="html-2503.14297" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14297" title="Other formats" id="oth-2503.14297" aria-labelledby="oth-2503.14297">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Improved Scalable Lipschitz Bounds for Deep Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Syed,+U">Usman Syed</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+B">Bin Hu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Systems and Control (eess.SY); Optimization and Control (math.OC); Machine Learning (stat.ML) </div> <p class='mathjax'> Computing tight Lipschitz bounds for deep neural networks is crucial for analyzing their robustness and stability, but existing approaches either produce relatively conservative estimates or rely on semidefinite programming (SDP) formulations (namely the LipSDP condition) that face scalability issues. Building upon ECLipsE-Fast, the state-of-the-art Lipschitz bound method that avoids SDP formulations, we derive a new family of improved scalable Lipschitz bounds that can be combined to outperform ECLipsE-Fast. Specifically, we leverage more general parameterizations of feasible points of LipSDP to derive various closed-form Lipschitz bounds, avoiding the use of SDP solvers. In addition, we show that our technique encompasses ECLipsE-Fast as a special case and leads to a much larger class of scalable Lipschitz bounds for deep neural networks. Our empirical study shows that our bounds improve ECLipsE-Fast, further advancing the scalability and precision of Lipschitz estimation for large neural networks. </p> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/2503.14328" title="Abstract" id="2503.14328"> arXiv:2503.14328 </a> (cross-list from math.OC) [<a href="/pdf/2503.14328" title="Download PDF" id="pdf-2503.14328" aria-labelledby="pdf-2503.14328">pdf</a>, <a href="https://arxiv.org/html/2503.14328v1" title="View HTML" id="html-2503.14328" aria-labelledby="html-2503.14328" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14328" title="Other formats" id="oth-2503.14328" aria-labelledby="oth-2503.14328">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Risk-Sensitive Model Predictive Control for Interaction-Aware Planning -- A Sequential Convexification Algorithm </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Wang,+R">Renzi Wang</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Schuurmans,+M">Mathijs Schuurmans</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Patrinos,+P">Panagiotis Patrinos</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Optimization and Control (math.OC)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing us to efficiently solve the generally nonconvex optimal control problem as a sequence of convex problems. Simulations of a robot navigating a corridor demonstrate the effectiveness and the computational advantage of the proposed approach. </p> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/2503.14331" title="Abstract" id="2503.14331"> arXiv:2503.14331 </a> (cross-list from cs.RO) [<a href="/pdf/2503.14331" title="Download PDF" id="pdf-2503.14331" aria-labelledby="pdf-2503.14331">pdf</a>, <a href="https://arxiv.org/html/2503.14331v1" title="View HTML" id="html-2503.14331" aria-labelledby="html-2503.14331" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14331" title="Other formats" id="oth-2503.14331" aria-labelledby="oth-2503.14331">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ADAPT: An Autonomous Forklift for Construction Site Operation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Huemer,+J">Johannes Huemer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Murschitz,+M">Markus Murschitz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sch%C3%B6rghuber,+M">Matthias Sch枚rghuber</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Reisinger,+L">Lukas Reisinger</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kadiofsky,+T">Thomas Kadiofsky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Weidinger,+C">Christoph Weidinger</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Niedermeyer,+M">Mario Niedermeyer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Widy,+B">Benedikt Widy</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zeilinger,+M">Marcel Zeilinger</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Beleznai,+C">Csaba Beleznai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gl%C3%BCck,+T">Tobias Gl眉ck</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kugi,+A">Andreas Kugi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zips,+P">Patrik Zips</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY) </div> <p class='mathjax'> Efficient material logistics play a critical role in controlling costs and schedules in the construction industry. However, manual material handling remains prone to inefficiencies, delays, and safety risks. Autonomous forklifts offer a promising solution to streamline on-site logistics, reducing reliance on human operators and mitigating labor shortages. This paper presents the development and evaluation of the Autonomous Dynamic All-terrain Pallet Transporter (ADAPT), a fully autonomous off-road forklift designed for construction environments. Unlike structured warehouse settings, construction sites pose significant challenges, including dynamic obstacles, unstructured terrain, and varying weather conditions. To address these challenges, our system integrates AI-driven perception techniques with traditional approaches for decision making, planning, and control, enabling reliable operation in complex environments. We validate the system through extensive real-world testing, comparing its long-term performance against an experienced human operator across various weather conditions. We also provide a comprehensive analysis of challenges and key lessons learned, contributing to the advancement of autonomous heavy machinery. Our findings demonstrate that autonomous outdoor forklifts can operate near human-level performance, offering a viable path toward safer and more efficient construction logistics. </p> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/2503.14352" title="Abstract" id="2503.14352"> arXiv:2503.14352 </a> (cross-list from cs.RO) [<a href="/pdf/2503.14352" title="Download PDF" id="pdf-2503.14352" aria-labelledby="pdf-2503.14352">pdf</a>, <a href="https://arxiv.org/html/2503.14352v1" title="View HTML" id="html-2503.14352" aria-labelledby="html-2503.14352" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14352" title="Other formats" id="oth-2503.14352" aria-labelledby="oth-2503.14352">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Flying in Highly Dynamic Environments with End-to-end Learning Approach </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Fan,+X">Xiyu Fan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lu,+M">Minghao Lu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+B">Bowen Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lu,+P">Peng Lu</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Fan, X., Lu, M., Xu, B., & Lu, P. (2025). Flying in Highly Dynamic Environments With End-to-End Learning Approach. IEEE Robotics and Automation Letters </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> Obstacle avoidance for unmanned aerial vehicles like quadrotors is a popular research topic. Most existing research focuses only on static environments, and obstacle avoidance in environments with multiple dynamic obstacles remains challenging. This paper proposes a novel deep-reinforcement learning-based approach for the quadrotors to navigate through highly dynamic environments. We propose a lidar data encoder to extract obstacle information from the massive point cloud data from the lidar. Multi frames of historical scans will be compressed into a 2-dimension obstacle map while maintaining the obstacle features required. An end-to-end deep neural network is trained to extract the kinematics of dynamic and static obstacles from the obstacle map, and it will generate acceleration commands to the quadrotor to control it to avoid these obstacles. Our approach contains perception and navigating functions in a single neural network, which can change from a navigating state into a hovering state without mode switching. We also present simulations and real-world experiments to show the effectiveness of our approach while navigating in highly dynamic cluttered environments. </p> </div> </dd> </dl> <dl id='articles'> <h3>Replacement submissions (showing 22 of 22 entries)</h3> <dt> <a name='item33'>[33]</a> <a href ="/abs/2307.00637" title="Abstract" id="2307.00637"> arXiv:2307.00637 </a> (replaced) [<a href="/pdf/2307.00637" title="Download PDF" id="pdf-2307.00637" aria-labelledby="pdf-2307.00637">pdf</a>, <a href="https://arxiv.org/html/2307.00637v2" title="View HTML" id="html-2307.00637" aria-labelledby="html-2307.00637" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2307.00637" title="Other formats" id="oth-2307.00637" aria-labelledby="oth-2307.00637">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On Embedding B-Splines in Recursive State Estimation </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+K">Kailai Li</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 9 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> We present a principled study on establishing a probabilistic framework for continuous-time state estimation. B-splines are embedded into state-space modeling as a continuous-time intermediate, linking the state of recurrent control points with asynchronous sensor measurements. Based thereon, the spline-embedded recursive estimation scheme is established w.r.t. common sensor fusion tasks, and corresponding technique for modeling uncertain motion estimates is introduced. We evaluate the proposed estimation scheme using real-world-based synthesized data in a range-inertial setting. Numerical results demonstrate several advantages of spline embedding in recursive state estimation compared to classical discrete-time filtering approaches. </p> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2401.10785" title="Abstract" id="2401.10785"> arXiv:2401.10785 </a> (replaced) [<a href="/pdf/2401.10785" title="Download PDF" id="pdf-2401.10785" aria-labelledby="pdf-2401.10785">pdf</a>, <a href="https://arxiv.org/html/2401.10785v2" title="View HTML" id="html-2401.10785" aria-labelledby="html-2401.10785" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2401.10785" title="Other formats" id="oth-2401.10785" aria-labelledby="oth-2401.10785">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Composite learning backstepping control with guaranteed exponential stability and robustness </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Shi,+T">Tian Shi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wen,+C">Changyun Wen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Pan,+Y">Yongping Pan</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> Adaptive backstepping control provides a feasible solution to achieve asymptotic tracking for mismatched uncertain nonlinear systems. However, input-to-state stability depends on high-gain feedback generated by nonlinear damping terms, and closed-loop exponential stability with parameter convergence involves a stringent condition named persistent excitation (PE). This paper proposes a composite learning backstepping control (CLBC) strategy based on modular backstepping and high-order tuners to compensate for the transient process of parameter estimation and achieve closed-loop exponential stability without the nonlinear damping terms and the PE condition. A novel composite learning mechanism that maximizes the staged exciting strength is designed for parameter estimation, such that parameter convergence can be achieved under a condition of interval excitation (IE) or even partial IE that is strictly weaker than PE. An extra prediction error is employed in the adaptive law to ensure the transient performance without nonlinear damping terms. The exponential stability of the closed-loop system is proved rigorously under the partial IE or IE condition. Simulations have demonstrated the effectiveness and superiority of the proposed method in both parameter estimation and control compared to state-of-the-art methods. </p> </div> </dd> <dt> <a name='item35'>[35]</a> <a href ="/abs/2404.02568" title="Abstract" id="2404.02568"> arXiv:2404.02568 </a> (replaced) [<a href="/pdf/2404.02568" title="Download PDF" id="pdf-2404.02568" aria-labelledby="pdf-2404.02568">pdf</a>, <a href="/format/2404.02568" title="Other formats" id="oth-2404.02568" aria-labelledby="oth-2404.02568">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On future power system digital twins: A vision towards a standard architecture </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Zomerdijk,+W">Wouter Zomerdijk</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Palensky,+P">Peter Palensky</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=AlSkaif,+T">Tarek AlSkaif</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Vergara,+P+P">Pedro P. Vergara</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This version of the paper has been accepted for publication in a journal </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world. Digital twins (DT) are the key concept for this. This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain. By reviewing different definitions and applications, the authors present a new definition of DTs specifically tailored to power systems. Based on the proposed definition and extensive deliberations and consultations with distribution system operators, energy traders, and municipalities, the authors introduce a vision of a standard DT ecosystem architecture that offers services beyond real-time updates and can seamlessly integrate with existing transmission and distribution system operators' processes while reconciling with concepts such as microgrids and local energy communities based on a system-of-systems view. The authors also discuss their vision related to the integration of power system DTs into various phases of the system's life cycle, such as long-term planning, emphasising challenges that remain to be addressed, such as managing measurement and model errors, and uncertainty propagation. Finally, the authors present their vision of how artificial intelligence and machine learning can enhance several power systems DT modules established in the proposed architecture. </p> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2410.22044" title="Abstract" id="2410.22044"> arXiv:2410.22044 </a> (replaced) [<a href="/pdf/2410.22044" title="Download PDF" id="pdf-2410.22044" aria-labelledby="pdf-2410.22044">pdf</a>, <a href="https://arxiv.org/html/2410.22044v2" title="View HTML" id="html-2410.22044" aria-labelledby="html-2410.22044" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.22044" title="Other formats" id="oth-2410.22044" aria-labelledby="oth-2410.22044">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Average Predictor-Feedback Control Design for Switched Linear Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Katsanikakis,+A">Andreas Katsanikakis</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Bekiaris-Liberis,+N">Nikolaos Bekiaris-Liberis</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Bresch-Pietri,+D">Delphine Bresch-Pietri</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 6 figures, submitted to 2025 IFAC Workshop on Time Delay Systems (TDS) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> We develop an input delay-compensating feedback law for linear switched systems with time-dependent switching. Because the future values of the switching signal, which are needed for constructing an exact predictor-feedback law, may be unavailable at current time, the key design challenge is how to construct a proper predictor state. We resolve this challenge constructing an average predictor-based feedback law, which may be viewed as an exact predictor-feedback law for a particular average system without switching. We establish that, under the predictor-based control law introduced, the closed-loop system is exponentially stable, provided that the plant's parameters are sufficiently close to the corresponding parameters of the average system. In particular, the allowable difference is inversely proportional to the size of delay and proportional to the dwell time of the switching signal. Since no restriction is imposed on the size of delay or dwell time themselves, such a limitation on the parameters of each mode is inherent to the problem considered (in which no a priori information on the switching signal is available), and thus, it cannot be removed. The stability proof relies on two main ingredients-a Lyapunov functional constructed via backstepping and derivation of solutions' estimates for the difference between the average and the exact predictor states. We present consistent, numerical simulation results, which illustrate the necessity of employing the average predictor-based law for achieving stabilization and desired performance of the closed-loop system. </p> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/2411.10359" title="Abstract" id="2411.10359"> arXiv:2411.10359 </a> (replaced) [<a href="/pdf/2411.10359" title="Download PDF" id="pdf-2411.10359" aria-labelledby="pdf-2411.10359">pdf</a>, <a href="/format/2411.10359" title="Other formats" id="oth-2411.10359" aria-labelledby="oth-2411.10359">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Koopman-based control of nonlinear systems with closed-loop guarantees </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Str%C3%A4sser,+R">Robin Str盲sser</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Berberich,+J">Julian Berberich</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Schaller,+M">Manuel Schaller</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Worthmann,+K">Karl Worthmann</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Allg%C3%B6wer,+F">Frank Allg枚wer</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted for publication in at-Automatisierungstechnik </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Optimization and Control (math.OC) </div> <p class='mathjax'> In this paper, we provide a tutorial overview and an extension of a recently developed framework for data-driven control of unknown nonlinear systems with rigorous closed-loop guarantees. The proposed approach relies on the Koopman operator representation of the nonlinear system, for which a bilinear surrogate model is estimated based on data. In contrast to existing Koopman-based estimation procedures, we state guaranteed bounds on the approximation error using the stability- and certificate-oriented extended dynamic mode decomposition (SafEDMD) framework. The resulting surrogate model and the uncertainty bounds allow us to design controllers via robust control theory and sum-of-squares optimization, guaranteeing desirable properties for the closed-loop system. We present results on stabilization both in discrete and continuous time, and we derive a method for controller design with performance objectives. The benefits of the presented framework over established approaches are demonstrated with a numerical example. </p> </div> </dd> <dt> <a name='item38'>[38]</a> <a href ="/abs/2412.19131" title="Abstract" id="2412.19131"> arXiv:2412.19131 </a> (replaced) [<a href="/pdf/2412.19131" title="Download PDF" id="pdf-2412.19131" aria-labelledby="pdf-2412.19131">pdf</a>, <a href="https://arxiv.org/html/2412.19131v2" title="View HTML" id="html-2412.19131" aria-labelledby="html-2412.19131" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2412.19131" title="Other formats" id="oth-2412.19131" aria-labelledby="oth-2412.19131">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Synthetic Discrete Inertia </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Vaca,+A">A. Vaca</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Milano,+F">F. Milano</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> This letter demonstrates how synthetic inertia can be obtained with the control of flexible discrete devices to keep the power balance of power systems, even if the system does not include any synchronous generator or conventional grid-forming converter. The letter also discusses solutions to cycling issues, which can arise due to the interaction of uncoordinated discrete inertia controllers. The effectiveness, dynamic performance, and challenges of the proposed approach are validated through simulations using modified versions of the WSCC 9-bus test system and of the all-island Irish transmission system. </p> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/2503.09865" title="Abstract" id="2503.09865"> arXiv:2503.09865 </a> (replaced) [<a href="/pdf/2503.09865" title="Download PDF" id="pdf-2503.09865" aria-labelledby="pdf-2503.09865">pdf</a>, <a href="https://arxiv.org/html/2503.09865v2" title="View HTML" id="html-2503.09865" aria-labelledby="html-2503.09865" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.09865" title="Other formats" id="oth-2503.09865" aria-labelledby="oth-2503.09865">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Identification and Classification of Human Performance related Challenges during Remote Driving </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Hans,+O">Ole Hans</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Adamy,+J">J眉rgen Adamy</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This work has been submitted to the IEEE for possible publication </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> Remote driving of vehicles is gaining in importance in the transportation sector, especially when Automated Driving Systems (ADSs) reach the limits of their system boundaries. This study investigates the challenges faced by human Remote Drivers (RDs) during remote driving, particularly focusing on the identification and classification of human performance-related challenges through a comprehensive analysis of real-world remote driving data Las Vegas. For this purpose, a total of 183 RD performance-related Safety Driver (SD) interventions were analyzed and classified using an introduced severity classification. As it is essential to prevent the need for SD interventions, this study identified and analyzed harsh driving events to detect an increased likelihood of interventions by the SD. In addition, the results of the subjective RD questionnaire are used to evaluate whether the objective metrics from SD interventions and harsh driving events can also be confirmed by the RDs and whether additional challenges can be uncovered. The analysis reveals learning curves, showing a significant decrease in SD interventions as RD experience increases. Early phases of remote driving experience, especially below 200 km of experience, showed the highest frequency of safety-related events, including braking late for traffic signs and responding impatiently to other traffic participants. Over time, RDs follow defined rules for improving their control, with experience leading to less harsh braking, acceleration, and steering maneuvers. The study contributes to understanding the requirements of RDS, emphasizing the importance of targeted training to address human performance limitations. It further highlights the need for system improvements to address challenges like latency and the limited haptic feedback replaced by visual feedback, which affect the RDs' perception and vehicle control. </p> </div> </dd> <dt> <a name='item40'>[40]</a> <a href ="/abs/2503.11021" title="Abstract" id="2503.11021"> arXiv:2503.11021 </a> (replaced) [<a href="/pdf/2503.11021" title="Download PDF" id="pdf-2503.11021" aria-labelledby="pdf-2503.11021">pdf</a>, <a href="https://arxiv.org/html/2503.11021v2" title="View HTML" id="html-2503.11021" aria-labelledby="html-2503.11021" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.11021" title="Other formats" id="oth-2503.11021" aria-labelledby="oth-2503.11021">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Approximate Hamilton-Jacobi Reachability Analysis for a Class of Two-Timescale Systems, with Application to Biological Models </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Hirsch,+D">Dylan Hirsch</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Herbert,+S">Sylvia Herbert</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Second version (note that title changed from previous version) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Molecular Networks (q-bio.MN) </div> <p class='mathjax'> Hamilton-Jacobi reachability (HJR) is an exciting framework used for control of safety-critical systems with nonlinear and possibly uncertain dynamics. However, HJR suffers from the curse of dimensionality, with computation times growing exponentially in the dimension of the system state. Many autonomous and controlled systems involve dynamics that evolve on multiple timescales, and for these systems, singular perturbation methods can be used for model reduction. However, such methods are more challenging to apply in HJR due to the presence of an underlying differential game. In this work, we leverage prior work on singularly perturbed differential games to identify a class of systems which can be readily reduced, and we relate these results to the quantities of interest in HJR. We demonstrate the utility of our results on two examples involving biological systems, where dynamics fitting the identified class are frequently encountered. </p> </div> </dd> <dt> <a name='item41'>[41]</a> <a href ="/abs/2503.12566" title="Abstract" id="2503.12566"> arXiv:2503.12566 </a> (replaced) [<a href="/pdf/2503.12566" title="Download PDF" id="pdf-2503.12566" aria-labelledby="pdf-2503.12566">pdf</a>, <a href="https://arxiv.org/html/2503.12566v2" title="View HTML" id="html-2503.12566" aria-labelledby="html-2503.12566" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.12566" title="Other formats" id="oth-2503.12566" aria-labelledby="oth-2503.12566">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Automotive Battery Pack Standards and Design Characteristics: A Review </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Haghbin,+S">Saeid Haghbin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Larijani,+M+R">Morteza Rezaei Larijani</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zolghadri,+M">MohammadReza Zolghadri</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Kia,+S+H">Shahin Hedayati Kia</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> This paper outlines the existing situation and future trends related to automobile battery packs, specifically from the automobile manufacturer's point of view. It formulates the specifications required for such packs to adhere to prevailing regulatory schemes and examines top-level solutions to target a uniform architecture for passenger cars. Key elements such as electrical performance, safety, mechanical integrity, reliability, environmental issues, diagnostics, and real-world implications have been extensively examined. This paper draws attention to the industry trend of shifting to high-voltage battery architectures to enable ultra-fast charging above 350 kW, reducing the charging time to less than 20 minutes. Technological advancements in energy density and battery pack capacities are poised to take electric vehicle ranges over 1000 km from a single charge. This study also examines developments in artificial intelligence-improved battery management systems, enhanced safety, mechanical integrity, reliability, diagnostics, and practical considerations. Furthermore, future developments, such as the incorporation of batteries in aviation and other new uses, are investigated to provide insight into the future generation of economically viable, secure, and high-performance battery systems. </p> </div> </dd> <dt> <a name='item42'>[42]</a> <a href ="/abs/2010.02990" title="Abstract" id="2010.02990"> arXiv:2010.02990 </a> (replaced) [<a href="/pdf/2010.02990" title="Download PDF" id="pdf-2010.02990" aria-labelledby="pdf-2010.02990">pdf</a>, <a href="https://arxiv.org/html/2010.02990v5" title="View HTML" id="html-2010.02990" aria-labelledby="html-2010.02990" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2010.02990" title="Other formats" id="oth-2010.02990" aria-labelledby="oth-2010.02990">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On The Convergence of Euler Discretization of Finite-Time Convergent Gradient Flows </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+S">Siqi Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benosman,+M">Mouhacine Benosman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Romero,+O">Orlando Romero</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> In this study, we investigate the performance of two novel first-order optimization algorithms, namely the rescaled-gradient flow (RGF) and the signed-gradient flow (SGF). These algorithms are derived from the forward Euler discretization of finite-time convergent flows, comprised of non-Lipschitz dynamical systems, which locally converge to the minima of gradient-dominated functions. We first characterize the closeness between the continuous flows and the discretizations, then we proceed to present (linear) convergence guarantees of the discrete algorithms (in the general and the stochastic case). Furthermore, in cases where problem parameters remain unknown or exhibit non-uniformity, we further integrate the line-search strategy with RGF/SGF and provide convergence analysis in this setting. We then apply the proposed algorithms to academic examples and deep neural network training, our results show that our schemes demonstrate faster convergences against standard optimization alternatives. </p> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2310.07649" title="Abstract" id="2310.07649"> arXiv:2310.07649 </a> (replaced) [<a href="/pdf/2310.07649" title="Download PDF" id="pdf-2310.07649" aria-labelledby="pdf-2310.07649">pdf</a>, <a href="https://arxiv.org/html/2310.07649v3" title="View HTML" id="html-2310.07649" aria-labelledby="html-2310.07649" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2310.07649" title="Other formats" id="oth-2310.07649" aria-labelledby="oth-2310.07649">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Automated Layout and Control Co-Design of Robust Multi-UAV Transportation Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bosio,+C">Carlo Bosio</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mueller,+M+W">Mark W. Mueller</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 7 pages, 7 figures, journal paper (IEEE RA-L) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> The joint optimization of physical parameters and controllers in robotic systems is challenging. This is due to the difficulties of predicting the effect that changes in physical parameters have on final performances. At the same time, physical and morphological modifications can improve robot capabilities, perhaps completely unlocking new skills and tasks. We present a novel approach to co-optimize the physical layout and the control of a cooperative aerial transportation system. The goal is to achieve the most precise and robust flight when carrying a payload. We assume the agents are connected to the payload through rigid attachments, essentially transforming the whole system into a larger flying object with ``thrust modules" at the attachment locations of the quadcopters. We investigate the optimal arrangement of the thrust modules around the payload, so that the resulting system achieves the best disturbance rejection capabilities. We propose a novel metric of robustness inspired by H2 control, and propose an algorithm to optimize the layout of the vehicles around the object and their controller altogether. We experimentally validate the effectiveness of our approach using fleets of three and four quadcopters and payloads of diverse shapes. </p> </div> </dd> <dt> <a name='item44'>[44]</a> <a href ="/abs/2403.13941" title="Abstract" id="2403.13941"> arXiv:2403.13941 </a> (replaced) [<a href="/pdf/2403.13941" title="Download PDF" id="pdf-2403.13941" aria-labelledby="pdf-2403.13941">pdf</a>, <a href="https://arxiv.org/html/2403.13941v3" title="View HTML" id="html-2403.13941" aria-labelledby="html-2403.13941" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.13941" title="Other formats" id="oth-2403.13941" aria-labelledby="oth-2403.13941">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sensory Glove-Based Surgical Robot User Interface </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Borgioli,+L">Leonardo Borgioli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Oh,+K">Ki-Hwan Oh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Valle,+V">Valentina Valle</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ducas,+A">Alvaro Ducas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Halloum,+M">Mohammad Halloum</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Medina,+D+F+M">Diego Federico Mendoza Medina</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sharifi,+A">Arman Sharifi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=L'opez,+P+A">Paula A L'opez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cassiani,+J">Jessica Cassiani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zefran,+M">Milos Zefran</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+L">Liaohai Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Giulianotti,+P+C">Pier Cristoforo Giulianotti</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 6 pages, 4 figures, 7 tables, submitted to International Conference on Robotics and Automation (ICRA) 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> Robotic surgery has reached a high level of maturity and has become an integral part of standard surgical care. However, existing surgeon consoles are bulky, take up valuable space in the operating room, make surgical team coordination challenging, and their proprietary nature makes it difficult to take advantage of recent technological advances, especially in virtual and augmented reality. One potential area for further improvement is the integration of modern sensory gloves into robotic platforms, allowing surgeons to control robotic arms intuitively with their hand movements. We propose one such system that combines an HTC Vive tracker, a Manus Meta Prime 3 XR sensory glove, and SCOPEYE wireless smart glasses. The system controls one arm of a da Vinci surgical robot. In addition to moving the arm, the surgeon can use fingers to control the end-effector of the surgical instrument. Hand gestures are used to implement clutching and similar functions. In particular, we introduce clutching of the instrument orientation, a functionality unavailable in the da Vinci system. The vibrotactile elements of the glove are used to provide feedback to the user when gesture commands are invoked. A qualitative and quantitative evaluation has been conducted that compares the current device with the dVRK console. The system is shown to have excellent tracking accuracy, and the new interface allows surgeons to perform common surgical training tasks with minimal practice efficiently. </p> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2406.07728" title="Abstract" id="2406.07728"> arXiv:2406.07728 </a> (replaced) [<a href="/pdf/2406.07728" title="Download PDF" id="pdf-2406.07728" aria-labelledby="pdf-2406.07728">pdf</a>, <a href="https://arxiv.org/html/2406.07728v2" title="View HTML" id="html-2406.07728" aria-labelledby="html-2406.07728" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2406.07728" title="Other formats" id="oth-2406.07728" aria-labelledby="oth-2406.07728">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Visibility-Aware RRT* for Safety-Critical Navigation of Perception-Limited Robots in Unknown Environments </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+T">Taekyung Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Panagou,+D">Dimitra Panagou</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to IEEE Robotics and Automation Letters (to be presented at IROS 2025). Our project page can be found at: <a href="https://www.taekyung.me/visibility-rrt" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> Safe autonomous navigation in unknown environments remains a critical challenge for robots with limited sensing capabilities. While safety-critical control techniques, such as Control Barrier Functions (CBFs), have been proposed to ensure safety, their effectiveness relies on the assumption that the robot has complete knowledge of its surroundings. In reality, robots often operate with restricted field-of-view and finite sensing range, which can lead to collisions with unknown obstacles if the planner is agnostic to these limitations. To address this issue, we introduce the Visibility-Aware RRT* algorithm that combines sampling-based planning with CBFs to generate safe and efficient global reference paths in partially unknown environments. The algorithm incorporates a collision avoidance CBF and a novel visibility CBF, which guarantees that the robot remains within locally collision-free regions, enabling timely detection and avoidance of unknown obstacles. We conduct extensive experiments interfacing the path planners with two different safety-critical controllers, wherein our method outperforms all other compared baselines across both safety and efficiency aspects. </p> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2407.17226" title="Abstract" id="2407.17226"> arXiv:2407.17226 </a> (replaced) [<a href="/pdf/2407.17226" title="Download PDF" id="pdf-2407.17226" aria-labelledby="pdf-2407.17226">pdf</a>, <a href="https://arxiv.org/html/2407.17226v3" title="View HTML" id="html-2407.17226" aria-labelledby="html-2407.17226" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2407.17226" title="Other formats" id="oth-2407.17226" aria-labelledby="oth-2407.17226">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+Y">Yilie Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jia,+Y">Yanwei Jia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+X+Y">Xun Yu Zhou</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 49 pages, 4 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Optimization and Control (math.OC) </div> <p class='mathjax'> We study reinforcement learning (RL) for a class of continuous-time linear-quadratic (LQ) control problems for diffusions, where states are scalar-valued and running control rewards are absent but volatilities of the state processes depend on both state and control variables. We apply a model-free approach that relies neither on knowledge of model parameters nor on their estimations, and devise an RL algorithm to learn the optimal policy parameter directly. Our main contributions include the introduction of an exploration schedule and a regret analysis of the proposed algorithm. We provide the convergence rate of the policy parameter to the optimal one, and prove that the algorithm achieves a regret bound of $O(N^{\frac{3}{4}})$ up to a logarithmic factor, where $N$ is the number of learning episodes. We conduct a simulation study to validate the theoretical results and demonstrate the effectiveness and reliability of the proposed algorithm. We also perform numerical comparisons between our method and those of the recent model-based stochastic LQ RL studies adapted to the state- and control-dependent volatility setting, demonstrating a better performance of the former in terms of regret bounds. </p> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/2409.13334" title="Abstract" id="2409.13334"> arXiv:2409.13334 </a> (replaced) [<a href="/pdf/2409.13334" title="Download PDF" id="pdf-2409.13334" aria-labelledby="pdf-2409.13334">pdf</a>, <a href="https://arxiv.org/html/2409.13334v2" title="View HTML" id="html-2409.13334" aria-labelledby="html-2409.13334" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2409.13334" title="Other formats" id="oth-2409.13334" aria-labelledby="oth-2409.13334">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Cooperative distributed model predictive control for embedded systems: Experiments with hovercraft formations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Stomberg,+G">G枚sta Stomberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schwan,+R">Roland Schwan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Grillo,+A">Andrea Grillo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jones,+C+N">Colin N. Jones</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Faulwasser,+T">Timm Faulwasser</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Systems and Control (eess.SY); Optimization and Control (math.OC) </div> <p class='mathjax'> This paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collectively solve a centralized optimal control problem in each sampling step via a stabilizing decentralized real-time iteration scheme using the alternating direction method of multipliers. The efficient implementation does not require a central coordinator, executes onboard the hovercraft, and facilitates sampling intervals in the millisecond range. The formation control experiments showcase the flexibility of the approach on scenarios with point-to-point transitions, trajectory tracking, collision avoidance, and moving obstacles. </p> </div> </dd> <dt> <a name='item48'>[48]</a> <a href ="/abs/2410.05406" title="Abstract" id="2410.05406"> arXiv:2410.05406 </a> (replaced) [<a href="/pdf/2410.05406" title="Download PDF" id="pdf-2410.05406" aria-labelledby="pdf-2410.05406">pdf</a>, <a href="/format/2410.05406" title="Other formats" id="oth-2410.05406" aria-labelledby="oth-2410.05406">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Synthesizing Interpretable Control Policies through Large Language Model Guided Search </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bosio,+C">Carlo Bosio</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mueller,+M+W">Mark W. Mueller</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 7 figures, conference paper </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Artificial Intelligence (cs.AI)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> The combination of Large Language Models (LLMs), systematic evaluation, and evolutionary algorithms has enabled breakthroughs in combinatorial optimization and scientific discovery. We propose to extend this powerful combination to the control of dynamical systems, generating interpretable control policies capable of complex behaviors. With our novel method, we represent control policies as programs in standard languages like Python. We evaluate candidate controllers in simulation and evolve them using a pre-trained LLM. Unlike conventional learning-based control techniques, which rely on black-box neural networks to encode control policies, our approach enhances transparency and interpretability. We still take advantage of the power of large AI models, but only at the policy design phase, ensuring that all system components remain interpretable and easily verifiable at runtime. Additionally, the use of standard programming languages makes it straightforward for humans to finetune or adapt the controllers based on their expertise and intuition. We illustrate our method through its application to the synthesis of an interpretable control policy for the pendulum swing-up and the ball in cup tasks. We make the code available at <a href="https://github.com/muellerlab/synthesizing_interpretable_control_policies.git" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item49'>[49]</a> <a href ="/abs/2410.07933" title="Abstract" id="2410.07933"> arXiv:2410.07933 </a> (replaced) [<a href="/pdf/2410.07933" title="Download PDF" id="pdf-2410.07933" aria-labelledby="pdf-2410.07933">pdf</a>, <a href="/format/2410.07933" title="Other formats" id="oth-2410.07933" aria-labelledby="oth-2410.07933">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Offline Hierarchical Reinforcement Learning via Inverse Optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Schmidt,+C">Carolin Schmidt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gammelli,+D">Daniele Gammelli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Harrison,+J">James Harrison</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pavone,+M">Marco Pavone</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rodrigues,+F">Filipe Rodrigues</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Systems and Control (eess.SY); Optimization and Control (math.OC) </div> <p class='mathjax'> Hierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon planning, and settings with sparse rewards. However, learning hierarchical policies from static offline datasets presents a significant challenge. Crucially, actions taken by higher-level policies may not be directly observable within hierarchical controllers, and the offline dataset might have been generated using a different policy structure, hindering the use of standard offline learning algorithms. In this work, we propose OHIO: a framework for offline reinforcement learning (RL) of hierarchical policies. Our framework leverages knowledge of the policy structure to solve the \textit{inverse problem}, recovering the unobservable high-level actions that likely generated the observed data under our hierarchical policy. This approach constructs a dataset suitable for off-the-shelf offline training. We demonstrate our framework on robotic and network optimization problems and show that it substantially outperforms end-to-end RL methods and improves robustness. We investigate a variety of instantiations of our framework, both in direct deployment of policies trained offline and when online fine-tuning is performed. Code and data are available at <a href="https://ohio-offline-hierarchical-rl.github.io" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </p> </div> </dd> <dt> <a name='item50'>[50]</a> <a href ="/abs/2411.06542" title="Abstract" id="2411.06542"> arXiv:2411.06542 </a> (replaced) [<a href="/pdf/2411.06542" title="Download PDF" id="pdf-2411.06542" aria-labelledby="pdf-2411.06542">pdf</a>, <a href="https://arxiv.org/html/2411.06542v3" title="View HTML" id="html-2411.06542" aria-labelledby="html-2411.06542" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.06542" title="Other formats" id="oth-2411.06542" aria-labelledby="oth-2411.06542">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans? </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Shirai,+Y">Yuki Shirai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+T">Tong Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Suh,+H+T">H.J. Terry Suh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhu,+H">Huaijiang Zhu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ni,+X">Xinpei Ni</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J">Jiuguang Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Simchowitz,+M">Max Simchowitz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pang,+T">Tao Pang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICRA2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Artificial Intelligence (cs.AI); Systems and Control (eess.SY) </div> <p class='mathjax'> Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effectively. However, applying classical control synthesis methods to smoothed contact dynamics remains relatively under-explored. This paper analyzes the efficacy of linear controller synthesis using differential simulators based on contact smoothing. We introduce natural baselines for leveraging contact smoothing to compute (a) open-loop plans robust to uncertain conditions and/or dynamics, and (b) feedback gains to stabilize around open-loop plans. Using robotic bimanual whole-body manipulation as a testbed, we perform extensive empirical experiments on over 300 trajectories and analyze why LQR seems insufficient for stabilizing contact-rich plans. The video summarizing this paper and hardware experiments is found here: <a href="https://youtu.be/HLaKi6qbwQg?si=_zCAmBBD6rGSitm9" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item51'>[51]</a> <a href ="/abs/2501.14009" title="Abstract" id="2501.14009"> arXiv:2501.14009 </a> (replaced) [<a href="/pdf/2501.14009" title="Download PDF" id="pdf-2501.14009" aria-labelledby="pdf-2501.14009">pdf</a>, <a href="https://arxiv.org/html/2501.14009v2" title="View HTML" id="html-2501.14009" aria-labelledby="html-2501.14009" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2501.14009" title="Other formats" id="oth-2501.14009" aria-labelledby="oth-2501.14009">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Scalable and Interpretable Verification of Image-based Neural Network Controllers for Autonomous Vehicles </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Parameshwaran,+A">Aditya Parameshwaran</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yue Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 5 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Systems and Control (eess.SY) </div> <p class='mathjax'> Existing formal verification methods for image-based neural network controllers in autonomous vehicles often struggle with high-dimensional inputs, computational inefficiency, and a lack of explainability. These challenges make it difficult to ensure safety and reliability, as processing high-dimensional image data is computationally intensive and neural networks are typically treated as black boxes. To address these issues, we propose SEVIN (Scalable and Explainable Verification of Image-Based Neural Network Controllers), a framework that leverages a Variational Autoencoders (VAE) to encode high-dimensional images into a lower-dimensional, explainable latent space. By annotating latent variables with corresponding control actions, we generate convex polytopes that serve as structured input spaces for verification, significantly reducing computational complexity and enhancing scalability. Integrating the VAE's decoder with the neural network controller allows for formal and robustness verification using these explainable polytopes. Our approach also incorporates robustness verification under real-world perturbations by augmenting the dataset and retraining the VAE to capture environmental variations. Experimental results demonstrate that SEVIN achieves efficient and scalable verification while providing explainable insights into controller behavior, bridging the gap between formal verification techniques and practical applications in safety-critical systems. </p> </div> </dd> <dt> <a name='item52'>[52]</a> <a href ="/abs/2502.12617" title="Abstract" id="2502.12617"> arXiv:2502.12617 </a> (replaced) [<a href="/pdf/2502.12617" title="Download PDF" id="pdf-2502.12617" aria-labelledby="pdf-2502.12617">pdf</a>, <a href="https://arxiv.org/html/2502.12617v2" title="View HTML" id="html-2502.12617" aria-labelledby="html-2502.12617" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.12617" title="Other formats" id="oth-2502.12617" aria-labelledby="oth-2502.12617">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Graph-Enhanced Deep-Reinforcement Learning Framework for the Aircraft Landing Problem </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Maru,+V">Vatsal Maru</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 27 pages, submitted to ESWA, comments are welcome </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Systems and Control (eess.SY) </div> <p class='mathjax'> The Aircraft Landing Problem (ALP) is one of the challenging problems in aircraft transportation and management. The challenge is to schedule the arriving aircraft in a sequence so that the cost and delays are optimized. There are various solution approaches to solving this problem, most of which are based on operations research algorithms and meta-heuristics. Although traditional methods perform better on one or the other factors, there remains a problem of solving real-time rescheduling and computational scalability altogether. This paper presents a novel deep reinforcement learning (DRL) framework that combines graph neural networks with actor-critic architectures to address the ALP. This paper introduces three key contributions: A graph-based state representation that efficiently captures temporal and spatial relationships between aircraft, a specialized actor-critic architecture designed to handle multiple competing objectives in landing scheduling, and a runway balance strategy that ensures efficient resource utilization while maintaining safety constraints. The results show that the trained algorithm can be tested on different problem sets and the results are competitive to operation research algorithms. The experimental results on standard benchmark data sets demonstrate a 99.95% reduction in computational time compared to Mixed Integer Programming (MIP) and 38% higher runway throughput over First Come First Serve (FCFS) approaches. Therefore, the proposed solution is competitive to traditional approaches and achieves substantial advancements. Notably, it does not require retraining, making it particularly suitable for industrial deployment. The frameworks capability to generate solutions within 1 second enables real-time rescheduling, addressing critical requirements of air traffic management. </p> </div> </dd> <dt> <a name='item53'>[53]</a> <a href ="/abs/2503.05839" title="Abstract" id="2503.05839"> arXiv:2503.05839 </a> (replaced) [<a href="/pdf/2503.05839" title="Download PDF" id="pdf-2503.05839" aria-labelledby="pdf-2503.05839">pdf</a>, <a href="https://arxiv.org/html/2503.05839v2" title="View HTML" id="html-2503.05839" aria-labelledby="html-2503.05839" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.05839" title="Other formats" id="oth-2503.05839" aria-labelledby="oth-2503.05839">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Enhancing AUTOSAR-Based Firmware Over-the-Air Updates in the Automotive Industry with a Practical Implementation on a Steering System </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mostafa,+M+A">Mostafa A. Mostafa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mohamed,+M+K">Mohamed K. Mohamed</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ezzat,+R+W">Radwa W. Ezzat</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Bachelor's thesis </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Cryptography and Security (cs.CR)</span>; Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY) </div> <p class='mathjax'> The automotive industry is increasingly reliant on software to manage complex vehicle functionalities, making efficient and secure firmware updates essential. Traditional firmware update methods, requiring physical connections through On-Board Diagnostics (OBD) ports, are inconvenient, costly, and time-consuming. Firmware Over-the-Air (FOTA) technology offers a revolutionary solution by enabling wireless updates, reducing operational costs, and enhancing the user experience. This project aims to design and implement an advanced FOTA system tailored for modern vehicles, incorporating the AUTOSAR architecture for scalability and standardization, and utilizing delta updating to minimize firmware update sizes, thereby improving bandwidth efficiency and reducing flashing times. To ensure security, the system integrates the UDS 0x27 protocol for authentication and data integrity during the update process. Communication between Electronic Control Units (ECUs) is achieved using the CAN protocol, while the ESP8266 module and the master ECU communicate via SPI for data transfer. The system's architecture includes key components such as a bootloader, boot manager, and bootloader updater to facilitate seamless firmware updates. The functionality of the system is demonstrated through two applications: a blinking LED and a Lane Keeping Assist (LKA) system, showcasing its versatility in handling critical automotive features. This project represents a significant step forward in automotive technology, offering a user-centric, efficient, and secure solution for automotive firmware management. </p> </div> </dd> <dt> <a name='item54'>[54]</a> <a href ="/abs/2503.09829" title="Abstract" id="2503.09829"> arXiv:2503.09829 </a> (replaced) [<a href="/pdf/2503.09829" title="Download PDF" id="pdf-2503.09829" aria-labelledby="pdf-2503.09829">pdf</a>, <a href="https://arxiv.org/html/2503.09829v2" title="View HTML" id="html-2503.09829" aria-labelledby="html-2503.09829" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.09829" title="Other formats" id="oth-2503.09829" aria-labelledby="oth-2503.09829">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Seo,+J">Joohwan Seo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yoo,+S">Soochul Yoo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chang,+J">Junwoo Chang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=An,+H">Hyunseok An</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ryu,+H">Hyunwoo Ryu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+S">Soomi Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kruthiventy,+A">Arvind Kruthiventy</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Choi,+J">Jongeun Choi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Horowitz,+R">Roberto Horowitz</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted to International Journcal of Control, Automation and Systems (IJCAS), Under Review </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Machine Learning (cs.LG); Systems and Control (eess.SY) </div> <p class='mathjax'> Recent advances in deep learning and Transformers have driven major breakthroughs in robotics by employing techniques such as imitation learning, reinforcement learning, and LLM-based multimodal perception and decision-making. However, conventional deep learning and Transformer models often struggle to process data with inherent symmetries and invariances, typically relying on large datasets or extensive data augmentation. Equivariant neural networks overcome these limitations by explicitly integrating symmetry and invariance into their architectures, leading to improved efficiency and generalization. This tutorial survey reviews a wide range of equivariant deep learning and control methods for robotics, from classic to state-of-the-art, with a focus on SE(3)-equivariant models that leverage the natural 3D rotational and translational symmetries in visual robotic manipulation and control design. Using unified mathematical notation, we begin by reviewing key concepts from group theory, along with matrix Lie groups and Lie algebras. We then introduce foundational group-equivariant neural network design and show how the group-equivariance can be obtained through their structure. Next, we discuss the applications of SE(3)-equivariant neural networks in robotics in terms of imitation learning and reinforcement learning. The SE(3)-equivariant control design is also reviewed from the perspective of geometric control. Finally, we highlight the challenges and future directions of equivariant methods in developing more robust, sample-efficient, and multi-modal real-world robotic systems. </p> </div> </dd> </dl> <div class='paging'>Total of 54 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/eess.SY/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">  <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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