CINXE.COM

<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"> <head> <title>Systems and Control </title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20240822" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script type="text/javascript" language="javascript">mathjaxToggle();</script> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" tabindex="-1" href="/IgnoreMe"></a> <div class="header-breadcrumbs"> <a href="/"><img src="/static/browse/0.3.4/images/arxiv-logo-one-color-white.svg" alt="arxiv logo" style="height:40px;"/></a> <span>></span> <a href="/list/eess.SY/recent">eess.SY</a> </div> <div class="search-block level-right"> <form class="level-item mini-search" method="GET" action="https://arxiv.org/search"> <div class="field has-addons"> <div class="control"> <input class="input is-small" type="text" name="query" placeholder="Search..." aria-label="Search term or terms" /> <p class="help"><a href="https://info.arxiv.org/help">Help</a> | <a href="https://arxiv.org/search/advanced">Advanced Search</a></p> </div> <div class="control"> <div class="select is-small"> <select name="searchtype" aria-label="Field to search"> <option value="all" selected="selected">All fields</option> <option value="title">Title</option> <option value="author">Author</option> <option value="abstract">Abstract</option> <option value="comments">Comments</option> <option value="journal_ref">Journal reference</option> <option value="acm_class">ACM classification</option> <option value="msc_class">MSC classification</option> <option value="report_num">Report number</option> <option value="paper_id">arXiv identifier</option> <option value="doi">DOI</option> <option value="orcid">ORCID</option> <option value="author_id">arXiv author ID</option> <option value="help">Help pages</option> <option value="full_text">Full text</option> </select> </div> </div> <input type="hidden" name="source" value="header"> <button class="button is-small is-cul-darker">Search</button> </div> </form> </div> </div> <div class="mobile-header"> <div class="columns is-mobile"> <div class="column logo-arxiv"><a href="https://arxiv.org/"><img src="/static/browse/0.3.4/images/arxiv-logomark-small-white.svg" alt="arXiv logo" style="height:60px;" /></a></div> <div class="column logo-cornell"><a href="https://www.cornell.edu/"> <picture> <source media="(min-width: 501px)" srcset="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg 400w" sizes="400w" /> <source srcset="/static/browse/0.3.4/images/icons/cu/cornell_seal_simple_black.svg 2x" /> <img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University Logo" /> </picture> </a></div> <div class="column nav" id="toggle-container" role="menubar"> <button class="toggle-control"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-white"><title>open search</title><path d="M505 442.7L405.3 343c-4.5-4.5-10.6-7-17-7H372c27.6-35.3 44-79.7 44-128C416 93.1 322.9 0 208 0S0 93.1 0 208s93.1 208 208 208c48.3 0 92.7-16.4 128-44v16.3c0 6.4 2.5 12.5 7 17l99.7 99.7c9.4 9.4 24.6 9.4 33.9 0l28.3-28.3c9.4-9.4 9.4-24.6.1-34zM208 336c-70.7 0-128-57.2-128-128 0-70.7 57.2-128 128-128 70.7 0 128 57.2 128 128 0 70.7-57.2 128-128 128z"/></svg></button> <div class="mobile-toggle-block toggle-target"> <form class="mobile-search-form" method="GET" action="https://arxiv.org/search"> <div class="field has-addons"> <input class="input" type="text" name="query" placeholder="Search..." aria-label="Search term or terms" /> <input type="hidden" name="source" value="header"> <input type="hidden" name="searchtype" value="all"> <button class="button">GO</button> </div> </form> </div> <button class="toggle-control"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-white" role="menu"><title>open navigation menu</title><path d="M16 132h416c8.837 0 16-7.163 16-16V76c0-8.837-7.163-16-16-16H16C7.163 60 0 67.163 0 76v40c0 8.837 7.163 16 16 16zm0 160h416c8.837 0 16-7.163 16-16v-40c0-8.837-7.163-16-16-16H16c-8.837 0-16 7.163-16 16v40c0 8.837 7.163 16 16 16zm0 160h416c8.837 0 16-7.163 16-16v-40c0-8.837-7.163-16-16-16H16c-8.837 0-16 7.163-16 16v40c0 8.837 7.163 16 16 16z"/ ></svg></button> <div class="mobile-toggle-block toggle-target"> <nav class="mobile-menu" aria-labelledby="mobilemenulabel"> <h2 id="mobilemenulabel">quick links</h2> <ul> <li><a href="https://arxiv.org/login">Login</a></li> <li><a href="https://info.arxiv.org/help">Help Pages</a></li> <li><a href="https://info.arxiv.org/about">About</a></li> </ul> </nav> </div> </div> </div> </div> </header> <main> <div id="content"> <div id='content-inner'> <div id='dlpage'> <h1>Systems and Control</h1> <ul> <li><a href="#item0">New submissions</a></li> <li><a href="#item9">Cross-lists</a></li> <li><a href="#item17">Replacements</a></li> </ul> <p>See <a id="recent-eess.SY" aria-labelledby="recent-eess.SY" href="/list/eess.SY/recent">recent</a> articles</p> <h3>Showing new listings for Monday, 25 November 2024</h3> <div class='paging'>Total of 23 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> <dl id='articles'> <h3>New submissions (showing 8 of 8 entries)</h3> <dt> <a name='item1'>[1]</a> <a href ="/abs/2411.14567" title="Abstract" id="2411.14567"> arXiv:2411.14567 </a> [<a href="/pdf/2411.14567" title="Download PDF" id="pdf-2411.14567" aria-labelledby="pdf-2411.14567">pdf</a>, <a href="/format/2411.14567" title="Other formats" id="oth-2411.14567" aria-labelledby="oth-2411.14567">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Energy Efficient Automated Driving as a GNEP: Vehicle-in-the-loop Experiments </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Bhattacharyya,+V">Viranjan Bhattacharyya</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Ard,+T">Tyler Ard</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+R">Rongyao Wang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Vahidi,+A">Ardalan Vahidi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Jia,+Y">Yunyi Jia</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Han,+J">Jihun Han</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 this paper, a multi-agent motion planning problem is studied aiming to minimize energy consumption of connected automated vehicles (CAVs) in lane change scenarios. We model this interactive motion planning as a generalized Nash equilibrium problem and formalize how vehicle-to-vehicle intention sharing enables solution of the game between multiple CAVs as an optimal control problem for each agent, to arrive at a generalized Nash equilibrium. The method is implemented via model predictive control (MPC) and compared with an advanced baseline MPC which utilizes unilateral predictions of other agents' future states. A ROS-based in-the-loop testbed is developed: the method is first evaluated in software-in-the-loop and then vehicle-in-the-loop experiments are conducted. Experimental results demonstrate energy and travel time benefits of the presented method in interactive lane change maneuvers. </p> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2411.14619" title="Abstract" id="2411.14619"> arXiv:2411.14619 </a> [<a href="/pdf/2411.14619" title="Download PDF" id="pdf-2411.14619" aria-labelledby="pdf-2411.14619">pdf</a>, <a href="https://arxiv.org/html/2411.14619v1" title="View HTML" id="html-2411.14619" aria-labelledby="html-2411.14619" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14619" title="Other formats" id="oth-2411.14619" aria-labelledby="oth-2411.14619">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Path Planning and Task Assignment for Data Retrieval from Wireless Sensor Nodes Relying on Game-Theoretic Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Papatheodorou,+S">Sotiris Papatheodorou</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Smyrnakis,+M">Michalis Smyrnakis</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hamidou,+T">Tembine Hamidou</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Tzes,+A">Anthony Tzes</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> In proceedings of the 5th International Conference on Control, Decision and Information Technologies, 2018. 6 pages, 5 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'> The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit a part of or the entire set of targets. Each segment of the tour relies on a minimum energy path planning algorithm. After the computation of all possible tour-segments, a utility function penalizing the overall energy consumption is formed. Rather than relying on the NP-hard Mobile Element Scheduling (MES) MILP problem, an approach using elements from game theory is employed. The suggested approach converges fast for most practical reasons thus allowing its utilization in near real time applications. Simulations are offered to highlight the efficiency of the developed algorithm. </p> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2411.14678" title="Abstract" id="2411.14678"> arXiv:2411.14678 </a> [<a href="/pdf/2411.14678" title="Download PDF" id="pdf-2411.14678" aria-labelledby="pdf-2411.14678">pdf</a>, <a href="https://arxiv.org/html/2411.14678v1" title="View HTML" id="html-2411.14678" aria-labelledby="html-2411.14678" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14678" title="Other formats" id="oth-2411.14678" aria-labelledby="oth-2411.14678">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Reinterpreting PID Controller From the Perspective of State Feedback and Lumped Disturbance Compensation </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Shi,+X">Xinyu Shi</a></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'> This paper analyzes the motion of solutions to non-homogeneous linear differential equations. It further clarifies that a proportional-integral-derivative (PID) controller essentially comprises two parts: a homogeneous controller and a disturbance observer, which are responsible for stabilizing the homogeneous system and compensating for the lumped disturbances (non-homogeneous components) of the system respectively. Based on this framework, the impact of measurement noise on control performance is examined, and a parameter tuning scheme for the traditional PID controller is provided. Finally, as examples, controllers are designed for two representative control problems: a trajectory tracking controller for an underactuated vertical takeoff and landing (VTOL) aircraft in the time domain, and a lateral controller for a vehicle in the distance domain. </p> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2411.14694" title="Abstract" id="2411.14694"> arXiv:2411.14694 </a> [<a href="/pdf/2411.14694" title="Download PDF" id="pdf-2411.14694" aria-labelledby="pdf-2411.14694">pdf</a>, <a href="https://arxiv.org/html/2411.14694v1" title="View HTML" id="html-2411.14694" aria-labelledby="html-2411.14694" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14694" title="Other formats" id="oth-2411.14694" aria-labelledby="oth-2411.14694">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Zheng,+K">Kedi Zheng</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Guo,+H">Hongye Guo</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Chen,+Q">Qixin Chen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Paper accepted for IEEE Transactions on Power Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> IEEE Transactions on Power Systems, vol. 38, no. 1, pp. 278-289, Jan. 2023 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Machine Learning (cs.LG) </div> <p class='mathjax'> This paper studies the pool strategy for price-makers under imperfect information. In this occasion, market participants cannot obtain essential transmission parameters of the power system. Thus, price-makers should estimate the market results with respect to their offer curves using available historical information. The linear programming model of economic dispatch is analyzed with the theory of rim multi-parametric linear programming (rim-MPLP). The characteristics of system patterns (combinations of status flags for generating units and transmission lines) are revealed. A multi-class classification model based on support vector machine (SVM) is trained to map the offer curves to system patterns, which is then integrated into the decision framework of the price-maker. The performance of the proposed method is validated on the IEEE 30-bus system, Illinois synthetic 200-bus system, and South Carolina synthetic 500-bus system. </p> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2411.14700" title="Abstract" id="2411.14700"> arXiv:2411.14700 </a> [<a href="/pdf/2411.14700" title="Download PDF" id="pdf-2411.14700" aria-labelledby="pdf-2411.14700">pdf</a>, <a href="https://arxiv.org/html/2411.14700v1" title="View HTML" id="html-2411.14700" aria-labelledby="html-2411.14700" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14700" title="Other formats" id="oth-2411.14700" aria-labelledby="oth-2411.14700">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering Lithium Battery Electrochemical Model </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Chen,+Y">Yuanbo Chen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zheng,+K">Kedi Zheng</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Gu,+Y">Yuxuan Gu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+J">Jianxiao Wang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Chen,+Q">Qixin Chen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Paper accepted for IEEE Transactions on Smart Grid. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> IEEE Transactions on Smart Grid, vol. 15, no. 3, pp. 3000-3015, May 2024 </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 grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article focuses on considering a refined battery model, i.e. the electrochemical model (EM), in the optimal dispatch of the local energy system with high penetration of EVs which replenish energy through V2G-equipped charge station and battery swapping station (BSS). In this paper, to utilize the EM efficiently, recursive EVB constraints and a corresponding matrix-based state update method are proposed based on EM power characterization. The charging EV state distribution is profiled and a multi-layer BSS model along with binary aggregation is proposed, in order to overcome the computation complexity of combining the refined battery constraints with the mixed integer optimization. Finally, a local energy system scenario is investigated for evaluation. The efficiency and effectiveness of EM consideration are assessed from the perspective of both the system and battery. </p> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2411.14707" title="Abstract" id="2411.14707"> arXiv:2411.14707 </a> [<a href="/pdf/2411.14707" title="Download PDF" id="pdf-2411.14707" aria-labelledby="pdf-2411.14707">pdf</a>, <a href="https://arxiv.org/html/2411.14707v1" title="View HTML" id="html-2411.14707" aria-labelledby="html-2411.14707" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14707" title="Other formats" id="oth-2411.14707" aria-labelledby="oth-2411.14707">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> High-Bandwidth, Low-Computational Approach: Estimator-Based Control for Hybrid Flying Capacitor Multilevel Converters Using Multi-Cost Gradient Descent and State Feedforward </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Hwang,+I">Inhwi Hwang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 18 pages, 18 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'> This paper presents an estimator-based control framework for hybrid flying capacitor multilevel (FCML) converters, achieving high-bandwidth control and reduced computational complexity. Utilizing a hybrid estimation method that combines closed-loop and open-loop dynamics, the proposed approach enables accurate and fast flying capacitor voltage estimation without relying on isolated voltage sensors or high-cost computing hardware. The methodology employs multi-cost gradient descent and state feedforward algorithms, enhancing estimation performance while maintaining low computational overhead. A detailed analysis of stability, gain setting, and rank-deficiency issues is provided, ensuring robust operation across diverse converter levels and duty cycle conditions. Simulation results validate the effectiveness of the proposed estimator in achieving active voltage balancing and current control with 6-level AC-DC buck FCML, contributing to cost-effective solutions for FCML applications, such as data centers and electric aircraft. </p> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2411.14756" title="Abstract" id="2411.14756"> arXiv:2411.14756 </a> [<a href="/pdf/2411.14756" title="Download PDF" id="pdf-2411.14756" aria-labelledby="pdf-2411.14756">pdf</a>, <a href="https://arxiv.org/html/2411.14756v1" title="View HTML" id="html-2411.14756" aria-labelledby="html-2411.14756" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14756" title="Other formats" id="oth-2411.14756" aria-labelledby="oth-2411.14756">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> KPG 193: A Synthetic Korean Power Grid Test System for Decarbonization Studies </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Song,+G">Geonho Song</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Kim,+J">Jip Kim</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 the 193 bus synthetic Korean power grid (KPG 193), developed using open data sources to address recent challenges of the Korean power system. The KPG 193 test system serves as a valuable platform for decarbonization research, capturing Korean low renewable energy penetration, concentrated urban energy demand, and isolated grid structure. Clustering techniques were applied to preserve key system characteristics while maintaining computational tractability and representativeness. The system includes 193 buses, 123 generators, 407 transmission lines, and incorporates temporal weather datasets. Its feasibility was validated through Unit Commitment (UC), DC Optimal Power Flow (DCOPF) and AC Optimal Power Flow (ACOPF) simulations using 2022 demand and renewable generation data. This test system aims to provide a foundational framework for modeling and analyzing the Korean power grid. </p> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2411.15007" title="Abstract" id="2411.15007"> arXiv:2411.15007 </a> [<a href="/pdf/2411.15007" title="Download PDF" id="pdf-2411.15007" aria-labelledby="pdf-2411.15007">pdf</a>, <a href="https://arxiv.org/html/2411.15007v1" title="View HTML" id="html-2411.15007" aria-labelledby="html-2411.15007" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.15007" title="Other formats" id="oth-2411.15007" aria-labelledby="oth-2411.15007">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> FTA generation using GenAI with an Autonomy sensor Usecase </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Shetiya,+S+S">Sneha Sudhir Shetiya</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Garikapati,+D">Divya Garikapati</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Sohoni,+V">Veeraja Sohoni</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); Cryptography and Security (cs.CR); Machine Learning (cs.LG) </div> <p class='mathjax'> Functional safety forms an important aspect in the design of systems. Its emphasis on the automotive industry has evolved significantly over the years. Till date many methods have been developed to get appropriate FTA(Fault Tree analysis) for various scenarios and features pertaining to Autonomous Driving. This paper is an attempt to explore the scope of using Generative Artificial Intelligence(GenAI) in order to develop Fault Tree Analysis(FTA) with the use case of malfunction for the Lidar sensor in mind. We explore various available open source Large Language Models(LLM) models and then dive deep into one of them to study its responses and provide our analysis. This paper successfully shows the possibility to train existing Large Language models through Prompt Engineering for fault tree analysis for any Autonomy usecase aided with PlantUML tool. </p> </div> </dd> </dl> <dl id='articles'> <h3>Cross submissions (showing 8 of 8 entries)</h3> <dt> <a name='item9'>[9]</a> <a href ="/abs/2411.14557" title="Abstract" id="2411.14557"> arXiv:2411.14557 </a> (cross-list from cs.CR) [<a href="/pdf/2411.14557" title="Download PDF" id="pdf-2411.14557" aria-labelledby="pdf-2411.14557">pdf</a>, <a href="https://arxiv.org/html/2411.14557v1" title="View HTML" id="html-2411.14557" aria-labelledby="html-2411.14557" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14557" title="Other formats" id="oth-2411.14557" aria-labelledby="oth-2411.14557">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Privacy-Preserving Power Flow Analysis via Secure Multi-Party Computation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=von+der+Heyden,+J">Jonas von der Heyden</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schl%C3%BCter,+N">Nils Schl眉ter</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Binfet,+P">Philipp Binfet</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Asman,+M">Martin Asman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zdrallek,+M">Markus Zdrallek</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jager,+T">Tibor Jager</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Darup,+M+S">Moritz Schulze Darup</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Cryptography and Security (cs.CR)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal sensitive private information. Consequently, the adoption of smart meters is often restricted via legal means and hampered by limited user acceptance. Since metering data is beneficial for fault-free grid operation, power management, and resource allocation, applying privacy-preserving techniques to smart metering data is an important research problem. This work addresses this by using secure multi-party computation (SMPC), allowing multiple parties to jointly evaluate functions of their private inputs without revealing the latter. Concretely, we show how to perform power flow analysis on cryptographically hidden prosumer data. More precisely, we present a tailored solution to the power flow problem building on an SMPC implementation of Newtons method. We analyze the security of our approach in the universal composability framework and provide benchmarks for various grid types, threat models, and solvers. Our results indicate that secure multi-party computation can be able to alleviate privacy issues in smart grids in certain applications. </p> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2411.14593" title="Abstract" id="2411.14593"> arXiv:2411.14593 </a> (cross-list from cs.RO) [<a href="/pdf/2411.14593" title="Download PDF" id="pdf-2411.14593" aria-labelledby="pdf-2411.14593">pdf</a>, <a href="https://arxiv.org/html/2411.14593v1" title="View HTML" id="html-2411.14593" aria-labelledby="html-2411.14593" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14593" title="Other formats" id="oth-2411.14593" aria-labelledby="oth-2411.14593">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Systematic Study of Multi-Agent Deep Reinforcement Learning for Safe and Robust Autonomous Highway Ramp Entry </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Schester,+L">Larry Schester</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ortiz,+L+E">Luis E. Ortiz</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 9 pages, 9 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Systems and Control (eess.SY) </div> <p class='mathjax'> Vehicles today can drive themselves on highways and driverless robotaxis operate in major cities, with more sophisticated levels of autonomous driving expected to be available and become more common in the future. Yet, technically speaking, so-called "Level 5" (L5) operation, corresponding to full autonomy, has not been achieved. For that to happen, functions such as fully autonomous highway ramp entry must be available, and provide provably safe, and reliably robust behavior to enable full autonomy. We present a systematic study of a highway ramp function that controls the vehicles forward-moving actions to minimize collisions with the stream of highway traffic into which a merging (ego) vehicle enters. We take a game-theoretic multi-agent (MA) approach to this problem and study the use of controllers based on deep reinforcement learning (DRL). The virtual environment of the MA DRL uses self-play with simulated data where merging vehicles safely learn to control longitudinal position during a taper-type merge. The work presented in this paper extends existing work by studying the interaction of more than two vehicles (agents) and does so by systematically expanding the road scene with additional traffic and ego vehicles. While previous work on the two-vehicle setting established that collision-free controllers are theoretically impossible in fully decentralized, non-coordinated environments, we empirically show that controllers learned using our approach are nearly ideal when measured against idealized optimal controllers. </p> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/2411.14618" title="Abstract" id="2411.14618"> arXiv:2411.14618 </a> (cross-list from cs.LG) [<a href="/pdf/2411.14618" title="Download PDF" id="pdf-2411.14618" aria-labelledby="pdf-2411.14618">pdf</a>, <a href="https://arxiv.org/html/2411.14618v1" title="View HTML" id="html-2411.14618" aria-labelledby="html-2411.14618" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14618" title="Other formats" id="oth-2411.14618" aria-labelledby="oth-2411.14618">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mai,+V">Vincent Mai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pham,+Q+H">Quang Hung Pham</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Favrel,+A">Arthur Favrel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gauthier,+J">Jean-Philippe Gauthier</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gagnon,+M">Martin Gagnon</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'> Hydro-generating units (HGUs) play a crucial role in integrating intermittent renewable energy sources into the power grid due to their flexible operational capabilities. This evolving role has led to an increase in transient events, such as startups, which impose significant stresses on turbines, leading to increased turbine fatigue and a reduced operational lifespan. Consequently, optimizing startup sequences to minimize stresses is vital for hydropower utilities. However, this task is challenging, as stress measurements on prototypes can be expensive and time-consuming. To tackle this challenge, we propose an innovative automated approach to optimize the startup parameters of HGUs with a limited budget of measured startup sequences. Our method combines active learning and black-box optimization techniques, utilizing virtual strain sensors and dynamic simulations of HGUs. This approach was tested in real-time during an on-site measurement campaign on an instrumented Francis turbine prototype. The results demonstrate that our algorithm successfully identified an optimal startup sequence using only seven measured sequences. It achieves a remarkable 42% reduction in the maximum strain cycle amplitude compared to the standard startup sequence. This study paves the way for more efficient HGU startup optimization, potentially extending their operational lifespans. </p> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2411.14679" title="Abstract" id="2411.14679"> arXiv:2411.14679 </a> (cross-list from cs.LG) [<a href="/pdf/2411.14679" title="Download PDF" id="pdf-2411.14679" aria-labelledby="pdf-2411.14679">pdf</a>, <a href="https://arxiv.org/html/2411.14679v1" title="View HTML" id="html-2411.14679" aria-labelledby="html-2411.14679" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14679" title="Other formats" id="oth-2411.14679" aria-labelledby="oth-2411.14679">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Recursive Gaussian Process State Space Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zheng,+T">Tengjie Zheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cheng,+L">Lin Cheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gong,+S">Shengping Gong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+X">Xu Huang</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); Machine Learning (stat.ML) </div> <p class='mathjax'> Learning dynamical models from data is not only fundamental but also holds great promise for advancing principle discovery, time-series prediction, and controller design. Among various approaches, Gaussian Process State-Space Models (GPSSMs) have recently gained significant attention due to their combination of flexibility and interpretability. However, for online learning, the field lacks an efficient method suitable for scenarios where prior information regarding data distribution and model function is limited. To address this issue, this paper proposes a recursive GPSSM method with adaptive capabilities for both operating domains and Gaussian process (GP) hyperparameters. Specifically, we first utilize first-order linearization to derive a Bayesian update equation for the joint distribution between the system state and the GP model, enabling closed-form and domain-independent learning. Second, an online selection algorithm for inducing points is developed based on informative criteria to achieve lightweight learning. Third, to support online hyperparameter optimization, we recover historical measurement information from the current filtering distribution. Comprehensive evaluations on both synthetic and real-world datasets demonstrate the superior accuracy, computational efficiency, and adaptability of our method compared to state-of-the-art online GPSSM techniques. </p> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2411.14733" title="Abstract" id="2411.14733"> arXiv:2411.14733 </a> (cross-list from cs.LG) [<a href="/pdf/2411.14733" title="Download PDF" id="pdf-2411.14733" aria-labelledby="pdf-2411.14733">pdf</a>, <a href="https://arxiv.org/html/2411.14733v1" title="View HTML" id="html-2411.14733" aria-labelledby="html-2411.14733" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14733" title="Other formats" id="oth-2411.14733" aria-labelledby="oth-2411.14733">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> FLARE: FP-Less PTQ and Low-ENOB ADC Based AMS-PiM for Error-Resilient, Fast, and Efficient Transformer Acceleration </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yi,+D">Donghyeon Yi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+S">Seoyoung Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+J">Jongho Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+J">Junyoung Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ha,+S">Sohmyung Ha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chang,+I+J">Ik Joon Chang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Je,+M">Minkyu Je</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Image and Video Processing (eess.IV); Systems and Control (eess.SY) </div> <p class='mathjax'> Encoder-based transformers, powered by self-attention layers, have revolutionized machine learning with their context-aware representations. However, their quadratic growth in computational and memory demands presents significant bottlenecks. Analog-Mixed-Signal Process-in-Memory (AMS-PiM) architectures address these challenges by enabling efficient on-chip processing. Traditionally, AMS-PiM relies on Quantization-Aware Training (QAT), which is hardware-efficient but requires extensive retraining to adapt models to AMS-PiMs, making it increasingly impractical for transformer models. Post-Training Quantization (PTQ) mitigates this training overhead but introduces significant hardware inefficiencies. PTQ relies on dequantization-quantization (DQ-Q) processes, floating-point units (FPUs), and high-ENOB (Effective Number of Bits) analog-to-digital converters (ADCs). Particularly, High-ENOB ADCs scale exponentially in area and energy ($2^{ENOB}$), reduce sensing margins, and increase susceptibility to process, voltage, and temperature (PVT) variations, further compounding PTQ's challenges in AMS-PiM systems. To overcome these limitations, we propose RAP, an AMS-PiM architecture that eliminates DQ-Q processes, introduces FPU- and division-free nonlinear processing, and employs a low-ENOB-ADC-based sparse Matrix Vector multiplication technique. Using the proposed techniques, RAP improves error resiliency, area/energy efficiency, and computational speed while preserving numerical stability. Experimental results demonstrate that RAP outperforms state-of-the-art GPUs and conventional PiM architectures in energy efficiency, latency, and accuracy, making it a scalable solution for the efficient deployment of transformers. </p> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2411.14950" title="Abstract" id="2411.14950"> arXiv:2411.14950 </a> (cross-list from cs.RO) [<a href="/pdf/2411.14950" title="Download PDF" id="pdf-2411.14950" aria-labelledby="pdf-2411.14950">pdf</a>, <a href="https://arxiv.org/html/2411.14950v1" title="View HTML" id="html-2411.14950" aria-labelledby="html-2411.14950" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.14950" title="Other formats" id="oth-2411.14950" aria-labelledby="oth-2411.14950">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Trajectory Planning and Control for Robotic Magnetic Manipulation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Isitman,+O">Ogulcan Isitman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alcan,+G">Gokhan Alcan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kyrki,+V">Ville Kyrki</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 6 figures </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 magnetic manipulation offers a minimally invasive approach to gastrointestinal examinations through capsule endoscopy. However, controlling such systems using external permanent magnets (EPM) is challenging due to nonlinear magnetic interactions, especially when there are complex navigation requirements such as avoidance of sensitive tissues. In this work, we present a novel trajectory planning and control method incorporating dynamics and navigation requirements, using a single EPM fixed to a robotic arm to manipulate an internal permanent magnet (IPM). Our approach employs a constrained iterative linear quadratic regulator that considers the dynamics of the IPM to generate optimal trajectories for both the EPM and IPM. Extensive simulations and real-world experiments, motivated by capsule endoscopy operations, demonstrate the robustness of the method, showcasing resilience to external disturbances and precise control under varying conditions. The experimental results show that the IPM reaches the goal position with a maximum mean error of 0.18 cm and a standard deviation of 0.21 cm. This work introduces a unified framework for constrained trajectory optimization in magnetic manipulation, directly incorporating both the IPM's dynamics and the EPM's manipulability. </p> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2411.15036" title="Abstract" id="2411.15036"> arXiv:2411.15036 </a> (cross-list from cs.LG) [<a href="/pdf/2411.15036" title="Download PDF" id="pdf-2411.15036" aria-labelledby="pdf-2411.15036">pdf</a>, <a href="https://arxiv.org/html/2411.15036v1" title="View HTML" id="html-2411.15036" aria-labelledby="html-2411.15036" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.15036" title="Other formats" id="oth-2411.15036" aria-labelledby="oth-2411.15036">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Z">Zeyang Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Azizan,+N">Navid Azizan</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'> Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative tasks, demonstrating impressive performance and scalability. However, deploying MARL agents in real-world applications presents critical safety challenges. Current safe MARL algorithms are largely based on the constrained Markov decision process (CMDP) framework, which enforces constraints only on discounted cumulative costs and lacks an all-time safety assurance. Moreover, these methods often overlook the feasibility issue (the system will inevitably violate state constraints within certain regions of the constraint set), resulting in either suboptimal performance or increased constraint violations. To address these challenges, we propose a novel theoretical framework for safe MARL with $\textit{state-wise}$ constraints, where safety requirements are enforced at every state the agents visit. To resolve the feasibility issue, we leverage a control-theoretic notion of the feasible region, the controlled invariant set (CIS), characterized by the safety value function. We develop a multi-agent method for identifying CISs, ensuring convergence to a Nash equilibrium on the safety value function. By incorporating CIS identification into the learning process, we introduce a multi-agent dual policy iteration algorithm that guarantees convergence to a generalized Nash equilibrium in state-wise constrained cooperative Markov games, achieving an optimal balance between feasibility and performance. Furthermore, for practical deployment in complex high-dimensional systems, we propose $\textit{Multi-Agent Dual Actor-Critic}$ (MADAC), a safe MARL algorithm that approximates the proposed iteration scheme within the deep RL paradigm. Empirical evaluations on safe MARL benchmarks demonstrate that MADAC consistently outperforms existing methods, delivering much higher rewards while reducing constraint violations. </p> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2411.15130" title="Abstract" id="2411.15130"> arXiv:2411.15130 </a> (cross-list from cs.RO) [<a href="/pdf/2411.15130" title="Download PDF" id="pdf-2411.15130" aria-labelledby="pdf-2411.15130">pdf</a>, <a href="https://arxiv.org/html/2411.15130v1" title="View HTML" id="html-2411.15130" aria-labelledby="html-2411.15130" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.15130" title="Other formats" id="oth-2411.15130" aria-labelledby="oth-2411.15130">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cai,+J">Jiaze Cai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sangli,+V">Vishnu Sangli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+M">Mintae Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Koushil">Koushil Sreenath</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'> Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and adaptiveness of flapping-wing flight. We propose a model-free reinforcement learning (RL)-based framework for a high degree-of-freedom (DoF) bird-inspired flapping-wing robot that allows for multimodal flight and agile trajectory tracking. Stability analysis was performed on the closed-loop system comprising of the flapping-wing system and the RL policy. Additionally, simulation results demonstrate that the RL-based controller can successfully learn complex wing trajectory patterns, achieve stable flight, switch between flight modes spontaneously, and track different trajectories under various aerodynamic conditions. </p> </div> </dd> </dl> <dl id='articles'> <h3>Replacement submissions (showing 7 of 7 entries)</h3> <dt> <a name='item17'>[17]</a> <a href ="/abs/2311.05810" title="Abstract" id="2311.05810"> arXiv:2311.05810 </a> (replaced) [<a href="/pdf/2311.05810" title="Download PDF" id="pdf-2311.05810" aria-labelledby="pdf-2311.05810">pdf</a>, <a href="/format/2311.05810" title="Other formats" id="oth-2311.05810" aria-labelledby="oth-2311.05810">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Automated Lane Change via Adaptive Interactive MPC: Human-in-the-Loop Experiments </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Bhattacharyya,+V">Viranjan Bhattacharyya</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Vahidi,+A">Ardalan Vahidi</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 article presents a new optimal control-based interactive motion planning algorithm for an autonomous vehicle interacting with a human-driven vehicle. The ego vehicle solves a joint optimization problem for its motion planning involving costs and coupled constraints of both vehicles and applies its own actions. The non-convex feasible region and lane discipline are handled by introducing integer decision variables and the resulting optimization problem is a mixed-integer quadratic program (MIQP) which is implemented via model predictive control (MPC). Furthermore, the ego vehicle imputes the cost of human-driven neighboring vehicle (NV) using an inverse optimal control method based on Karush-Kuhn-Tucker (KKT) conditions and adapts the joint optimization cost accordingly. We call the algorithm adaptive interactive mixed-integer MPC (aiMPC). Its interaction with human subjects driving the NV in a mandatory lane change scenario is tested in a developed software-and-human-in-the-loop simulator. Results show the effectiveness of the presented algorithm in terms of enhanced mobility of both the vehicles compared to baseline methods. </p> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2312.04767" title="Abstract" id="2312.04767"> arXiv:2312.04767 </a> (replaced) [<a href="/pdf/2312.04767" title="Download PDF" id="pdf-2312.04767" aria-labelledby="pdf-2312.04767">pdf</a>, <a href="https://arxiv.org/html/2312.04767v3" title="View HTML" id="html-2312.04767" aria-labelledby="html-2312.04767" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2312.04767" title="Other formats" id="oth-2312.04767" aria-labelledby="oth-2312.04767">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Zhou,+M">Mi Zhou</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+J">Jiazhi Li</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Mortazavi,+M">Masood Mortazavi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yan,+N">Ning Yan</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Abdallah,+C">Chaouki Abdallah</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 this article, a \underline{S}tate-dependent \underline{M}ulti-\underline{A}gent \underline{D}eep \underline{D}eterministic \underline{P}olicy \underline{G}radient (\textbf{SMADDPG}) method is proposed in order to learn an optimal control policy for regionally switched systems. We observe good performance of this method and explain it in a rigorous mathematical language using some simplifying assumptions in order to motivate the ideas and to apply them to some canonical examples. Using reinforcement learning, the performance of the switched learning-based multi-agent method is compared with the vanilla DDPG in two customized demonstrative environments with one and two-dimensional state spaces. </p> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/2401.04487" title="Abstract" id="2401.04487"> arXiv:2401.04487 </a> (replaced) [<a href="/pdf/2401.04487" title="Download PDF" id="pdf-2401.04487" aria-labelledby="pdf-2401.04487">pdf</a>, <a href="/format/2401.04487" title="Other formats" id="oth-2401.04487" aria-labelledby="oth-2401.04487">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Online convex optimization for robust control of constrained dynamical systems </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Nonhoff,+M">Marko Nonhoff</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Dall'Anese,+E">Emiliano Dall'Anese</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=M%C3%BCller,+M+A">Matthias A. M眉ller</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 16 pages </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'> This article investigates the problem of controlling linear time-invariant systems subject to time-varying and a priori unknown cost functions, state and input constraints, and exogenous disturbances. We combine the online convex optimization framework with tools from robust model predictive control to propose an algorithm that is able to guarantee robust constraint satisfaction. The performance of the closed loop emerging from application of our framework is studied in terms of its dynamic regret, which is proven to be bounded linearly by the variation of the cost functions and the magnitude of the disturbances. We corroborate our theoretical findings and illustrate implementational aspects of the proposed algorithm by a numerical case study on a tracking control problem of an autonomous vehicle. </p> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2406.14861" title="Abstract" id="2406.14861"> arXiv:2406.14861 </a> (replaced) [<a href="/pdf/2406.14861" title="Download PDF" id="pdf-2406.14861" aria-labelledby="pdf-2406.14861">pdf</a>, <a href="/format/2406.14861" title="Other formats" id="oth-2406.14861" aria-labelledby="oth-2406.14861">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Resilience of the Electric Grid through Trustable IoT-Coordinated Assets </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Nair,+V+J">Vineet J. Nair</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Venkataramanan,+V">Venkatesh Venkataramanan</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Priyank">Priyank Srivastava</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Sarker,+P+S">Partha S. Sarker</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Anurag">Anurag Srivastava</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Marinovici,+L+D">Laurentiu D. Marinovici</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zha,+J">Jun Zha</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Irwin,+C">Christopher Irwin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Mittal,+P">Prateek Mittal</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Williams,+J">John Williams</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Kumar,+J">Jayant Kumar</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Poor,+H+V">H. Vincent Poor</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Annaswamy,+A+M">Anuradha M. Annaswamy</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to the Proceedings of the National Academy of Sciences (PNAS) 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Emerging Technologies (cs.ET) </div> <p class='mathjax'> The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things (IoT) devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity co-simulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator. </p> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2410.24172" title="Abstract" id="2410.24172"> arXiv:2410.24172 </a> (replaced) [<a href="/pdf/2410.24172" title="Download PDF" id="pdf-2410.24172" aria-labelledby="pdf-2410.24172">pdf</a>, <a href="/format/2410.24172" title="Other formats" id="oth-2410.24172" aria-labelledby="oth-2410.24172">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Multiphysics Analysis and Investigation of Soft Magnetics Effect on IPMSM: Case Study Dynamometer </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Amini,+A">Ali Amini</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=KhajueeZadeh,+M">MohammadSadegh KhajueeZadeh</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Vahedi,+A">Abolfazl Vahedi</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> ICEMG 2023 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span> </div> <p class='mathjax'> Nowadays, Interior Permanent Magnet Synchronous Motors (IPMSMs) are taken into attention in the industry owing to their advantages. Moreover, in many cases, performing static tests is not enough, and investigating electric machines under dynamic conditions is necessary. Accordingly, by employing a dynamometer system, the dynamic behavior of the electric machine under test is investigated. Among the dynamometers, the best is the Alternating (AC) dynamometer because the basic dynamometers cannot take loads with high complexity. So, in the following study, two IPMSM with V-type and Delta-type rotor configurations are designed and suggested to employ in AC dynamometer. Any non-ideality in the electric machines of AC dynamometers, electrically and mechanically, causes errors in the measurement of the motor under test. Electrically and mechanically, the behavior of a system significantly depends on the used soft magnetics besides its physical and magnetic configuration. Accordingly, by performing a Multiphysics analysis and using the FEM tool to change the soft magnetics in the rotor and stator core, comparing the electric motors' behavior in the AC dynamometer is investigated under the same operating conditions electrically and mechanically. Finally, which soft magnetics is more satisfactory for the AC dynamometer can be seen. </p> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2312.09384" title="Abstract" id="2312.09384"> arXiv:2312.09384 </a> (replaced) [<a href="/pdf/2312.09384" title="Download PDF" id="pdf-2312.09384" aria-labelledby="pdf-2312.09384">pdf</a>, <a href="https://arxiv.org/html/2312.09384v3" title="View HTML" id="html-2312.09384" aria-labelledby="html-2312.09384" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2312.09384" title="Other formats" id="oth-2312.09384" aria-labelledby="oth-2312.09384">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Modeling Epidemic Spread: A Gaussian Process Regression Approach </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=She,+B">Baike She</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Xin,+L">Lei Xin</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Par%C3%A9,+P+E">Philip E. Par茅</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Hale,+M">Matthew Hale</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> The code for the analyses is available at <a href="https://github.com/baikeshe/GPR_Epi_Modeling" 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">Machine Learning (stat.ML)</span>; Systems and Control (eess.SY); Physics and Society (physics.soc-ph) </div> <p class='mathjax'> Modeling epidemic spread is critical for informing policy decisions aimed at mitigation. Accordingly, in this work we present a new data-driven method based on Gaussian process regression (GPR) to model epidemic spread through the difference on the logarithmic scale of the infected cases. We bound the variance of the predictions made by GPR, which quantifies the impact of epidemic data on the proposed model. Next, we derive a high-probability error bound on the prediction error in terms of the distance between the training points and a testing point, the posterior variance, and the level of change in the spreading process, and we assess how the characteristics of the epidemic spread and infection data influence this error bound. We present examples that use GPR to model and predict epidemic spread by using real-world infection data gathered in the UK during the COVID-19 epidemic. These examples illustrate that, under typical conditions, the prediction for the next twenty days has 94.29% of the noisy data located within the 95% confidence interval, validating these predictions. We further compare the modeling and prediction results with other methods, such as polynomial regression, k-nearest neighbors (KNN) regression, and neural networks, to demonstrate the benefits of leveraging GPR in disease spread modeling. </p> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2404.03307" title="Abstract" id="2404.03307"> arXiv:2404.03307 </a> (replaced) [<a href="/pdf/2404.03307" title="Download PDF" id="pdf-2404.03307" aria-labelledby="pdf-2404.03307">pdf</a>, <a href="https://arxiv.org/html/2404.03307v3" title="View HTML" id="html-2404.03307" aria-labelledby="html-2404.03307" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2404.03307" title="Other formats" id="oth-2404.03307" aria-labelledby="oth-2404.03307">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Bi-level Trajectory Optimization on Uneven Terrains with Differentiable Wheel-Terrain Interaction Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Manoharan,+A">Amith Manoharan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sharma,+A">Aditya Sharma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Belsare,+H">Himani Belsare</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pal,+K">Kaustab Pal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Krishna,+K+M">K. Madhava Krishna</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Singh,+A+K">Arun Kumar Singh</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 7 figures, submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024) </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'> Navigation of wheeled vehicles on uneven terrain necessitates going beyond the 2D approaches for trajectory planning. Specifically, it is essential to incorporate the full 6dof variation of vehicle pose and its associated stability cost in the planning process. To this end, most recent works aim to learn a neural network model to predict the vehicle evolution. However, such approaches are data-intensive and fraught with generalization issues. In this paper, we present a purely model-based approach that just requires the digital elevation information of the terrain. Specifically, we express the wheel-terrain interaction and 6dof pose prediction as a non-linear least squares (NLS) problem. As a result, trajectory planning can be viewed as a bi-level optimization. The inner optimization layer predicts the pose on the terrain along a given trajectory, while the outer layer deforms the trajectory itself to reduce the stability and kinematic costs of the pose. We improve the state-of-the-art in the following respects. First, we show that our NLS based pose prediction closely matches the output from a high-fidelity physics engine. This result coupled with the fact that we can query gradients of the NLS solver, makes our pose predictor, a differentiable wheel-terrain interaction model. We further leverage this differentiability to efficiently solve the proposed bi-level trajectory optimization problem. Finally, we perform extensive experiments, and comparison with a baseline to showcase the effectiveness of our approach in obtaining smooth, stable trajectories. </p> </div> </dd> </dl> <div class='paging'>Total of 23 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; line-height: 2;"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><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>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>   </div> </footer> </div> <script src="/static/base/1.0.1/js/member_acknowledgement.js"></script> </body> </html>