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predictive control (MPC)</title> <meta name="description" content="Search results for: model predictive control (MPC)"> <meta name="keywords" content="model predictive control (MPC)"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="model predictive control (MPC)"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 25930</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: model predictive control (MPC)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25930</span> Application of Fractional Model Predictive Control to Thermal System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aymen%20Rhouma">Aymen Rhouma</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Hcheichi"> Khaled Hcheichi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sami%20Hafsi"> Sami Hafsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article presents an application of Fractional Model Predictive Control (FMPC) to a fractional order thermal system using Controlled Auto Regressive Integrated Moving Average (CARIMA) model obtained by discretization of a continuous fractional differential equation. Moreover, the output deviation approach is exploited to design the K -step ahead output predictor, and the corresponding control law is obtained by solving a quadratic cost function. Experiment results onto a thermal system are presented to emphasize the performances and the effectiveness of the proposed predictive controller<em>.</em> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractional%20model%20predictive%20control" title="fractional model predictive control">fractional model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20order%20systems" title=" fractional order systems"> fractional order systems</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20system" title=" thermal system"> thermal system</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title=" predictive control"> predictive control</a> </p> <a href="https://publications.waset.org/abstracts/66187/application-of-fractional-model-predictive-control-to-thermal-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66187.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">411</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25929</span> Model Predictive Control Using Thermal Inputs for Crystal Growth Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, crystal growth technologies have made progress by the requirement for the high quality of crystal materials. To control the crystal growth dynamics actively by external forces is useuful for reducing composition non-uniformity. In this study, a control method based on model predictive control using thermal inputs is proposed for crystal growth dynamics of semiconductor materials. The control system of crystal growth dynamics considered here is governed by the continuity, momentum, energy, and mass transport equations. To establish the control method for such thermal fluid systems, we adopt model predictive control known as a kind of optimal feedback control in which the control performance over a finite future is optimized with a performance index that has a moving initial time and terminal time. The objective of this study is to establish a model predictive control method for crystal growth dynamics of semiconductor materials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20control" title=" process control"> process control</a>, <a href="https://publications.waset.org/abstracts/search?q=crystal%20growth" title=" crystal growth"> crystal growth</a> </p> <a href="https://publications.waset.org/abstracts/88644/model-predictive-control-using-thermal-inputs-for-crystal-growth-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25928</span> Computational Simulations on Stability of Model Predictive Control for Linear Discrete-Time Stochastic Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial time and a moving terminal time. This paper examines the stability of model predictive control for linear discrete-time systems with additive stochastic disturbances. A sufficient condition for the stability of the closed-loop system with model predictive control is derived by means of a linear matrix inequality. The objective of this paper is to show the results of computational simulations in order to verify the validity of the obtained stability condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20simulations" title="computational simulations">computational simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title=" predictive control"> predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-time%20systems" title=" discrete-time systems"> discrete-time systems</a> </p> <a href="https://publications.waset.org/abstracts/35462/computational-simulations-on-stability-of-model-predictive-control-for-linear-discrete-time-stochastic-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25927</span> Temperature Control Improvement of Membrane Reactor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pornsiri%20Kaewpradit">Pornsiri Kaewpradit</a>, <a href="https://publications.waset.org/abstracts/search?q=Chalisa%20Pourneaw"> Chalisa Pourneaw</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Temperature control improvement of a membrane reactor with exothermic and reversible esterification reaction is studied in this work. It is well known that a batch membrane reactor requires different control strategies from a continuous one due to the fact that it is operated dynamically. Due to the effect of the operating temperature, the suitable control scheme has to be designed based reliable predictive model to achieve a desired objective. In the study, the optimization framework has been preliminary formulated in order to determine an optimal temperature trajectory for maximizing a desired product. In model predictive control scheme, a set of predictive models have been initially developed corresponding to the possible operating points of the system. The multiple predictive control moves have been further calculated on-line using the developed models corresponding to current operating point. It is obviously seen in the simulation results that the temperature control has been improved compared to the performance obtained by the conventional predictive controller. Further robustness tests have also been investigated in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=batch%20reactor" title=" batch reactor"> batch reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20control" title=" temperature control"> temperature control</a>, <a href="https://publications.waset.org/abstracts/search?q=membrane%20reactor" title=" membrane reactor "> membrane reactor </a> </p> <a href="https://publications.waset.org/abstracts/17487/temperature-control-improvement-of-membrane-reactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17487.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25926</span> Model Predictive Control of Three Phase Inverter for PV Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a three leg voltage source inverter (VSI). Operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a 35.7 kW PV system in Matlab/Simulink. Implementation and results show simplicity and accuracy, as well as reliability of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20phase%20voltage%20source%20inverter" title=" three phase voltage source inverter"> three phase voltage source inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=PV%20system" title=" PV system"> PV system</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab%2Fsimulink" title=" Matlab/simulink"> Matlab/simulink</a> </p> <a href="https://publications.waset.org/abstracts/40124/model-predictive-control-of-three-phase-inverter-for-pv-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40124.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">596</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25925</span> Space Vector PWM and Model Predictive Control for Voltage Source Inverter Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a comparative assessment of Space Vector Pulse Width Modulation (SVPWM) and Model Predictive Control (MPC) for two-level three phase (2L-3P) Voltage Source Inverter (VSI). VSI with associated system is subjected to both control techniques and the results are compared. Matlab/Simulink was used to model, simulate and validate the control schemes. Findings of this study show that MPC is superior to SVPWM in terms of total harmonic distortion (THD) and implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=voltage%20source%20inverter" title="voltage source inverter">voltage source inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20vector%20pulse%20width%20modulation" title=" space vector pulse width modulation"> space vector pulse width modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=comparison" title=" comparison"> comparison</a> </p> <a href="https://publications.waset.org/abstracts/16220/space-vector-pwm-and-model-predictive-control-for-voltage-source-inverter-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16220.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">508</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25924</span> Metabolic Predictive Model for PMV Control Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eunji%20Choi">Eunji Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Borang%20Park"> Borang Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Youngjae%20Choi"> Youngjae Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinwoo%20Moon"> Jinwoo Moon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=indoor%20quality" title=" indoor quality"> indoor quality</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolism" title=" metabolism"> metabolism</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20model" title=" predictive model"> predictive model</a> </p> <a href="https://publications.waset.org/abstracts/93271/metabolic-predictive-model-for-pmv-control-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93271.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25923</span> Systematic and Simple Guidance for Feed Forward Design in Model Predictive Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shukri%20Dughman">Shukri Dughman</a>, <a href="https://publications.waset.org/abstracts/search?q=Anthony%20Rossiter"> Anthony Rossiter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper builds on earlier work which demonstrated that Model Predictive Control (MPC) may give a poor choice of default feed forward compensator. By first demonstrating the impact of future information of target changes on the performance, this paper proposes a pragmatic method for identifying the amount of future information on the target that can be utilised effectively in both finite and infinite horizon algorithms. Numerical illustrations in MATLAB give evidence of the efficacy of the proposal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking%20control" title=" tracking control"> tracking control</a>, <a href="https://publications.waset.org/abstracts/search?q=advance%20knowledge" title=" advance knowledge"> advance knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=feed%20forward" title=" feed forward"> feed forward</a> </p> <a href="https://publications.waset.org/abstracts/36567/systematic-and-simple-guidance-for-feed-forward-design-in-model-predictive-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36567.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25922</span> Sampled-Data Model Predictive Tracking Control for Mobile Robot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wookyong%20Kwon">Wookyong Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangmoon%20Lee"> Sangmoon Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=sampled-data%20control" title=" sampled-data control"> sampled-data control</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20parameter%20varying%20systems" title=" linear parameter varying systems"> linear parameter varying systems</a>, <a href="https://publications.waset.org/abstracts/search?q=LPV" title=" LPV"> LPV</a> </p> <a href="https://publications.waset.org/abstracts/71683/sampled-data-model-predictive-tracking-control-for-mobile-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25921</span> Model Predictive Control of Turbocharged Diesel Engine with Exhaust Gas Recirculation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Yavas">U. Yavas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gokasan"> M. Gokasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control of diesel engine’s air path has drawn a lot of attention due to its multi input-multi output, closed coupled, non-linear relation. Today, precise control of amount of air to be combusted is a must in order to meet with tight emission limits and performance targets. In this study, passenger car size diesel engine is modeled by AVL Boost RT, and then simulated with standard, industry level PID controllers. Finally, linear model predictive control is designed and simulated. This study shows the importance of modeling and control of diesel engines with flexible algorithm development in computer based systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title="predictive control">predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=engine%20control" title=" engine control"> engine control</a>, <a href="https://publications.waset.org/abstracts/search?q=engine%20modeling" title=" engine modeling"> engine modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20control" title=" PID control"> PID control</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20compensation" title=" feedforward compensation"> feedforward compensation</a> </p> <a href="https://publications.waset.org/abstracts/34455/model-predictive-control-of-turbocharged-diesel-engine-with-exhaust-gas-recirculation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34455.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">636</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25920</span> Model Predictive Control with Unscented Kalman Filter for Nonlinear Implicit Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A class of implicit systems is known as a more generalized class of systems than a class of explicit systems. To establish a control method for such a generalized class of systems, we adopt model predictive control method which is a kind of optimal feedback control with a performance index that has a moving initial time and terminal time. However, model predictive control method is inapplicable to systems whose all state variables are not exactly known. In other words, model predictive control method is inapplicable to systems with limited measurable states. In fact, it is usual that the state variables of systems are measured through outputs, hence, only limited parts of them can be used directly. It is also usual that output signals are disturbed by process and sensor noises. Hence, it is important to establish a state estimation method for nonlinear implicit systems with taking the process noise and sensor noise into consideration. To this purpose, we apply the model predictive control method and unscented Kalman filter for solving the optimization and estimation problems of nonlinear implicit systems, respectively. The objective of this study is to establish a model predictive control with unscented Kalman filter for nonlinear implicit systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/97739/model-predictive-control-with-unscented-kalman-filter-for-nonlinear-implicit-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97739.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25919</span> RBF Modelling and Optimization Control for Semi-Batch Reactors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Magdi%20M.%20Nabi">Magdi M. Nabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ding-Li%20Yu"> Ding-Li Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chylla-Haase%20reactor" title="Chylla-Haase reactor">Chylla-Haase reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20network%20modelling" title=" RBF neural network modelling"> RBF neural network modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-batch%20reactors" title=" semi-batch reactors"> semi-batch reactors</a> </p> <a href="https://publications.waset.org/abstracts/11884/rbf-modelling-and-optimization-control-for-semi-batch-reactors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11884.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25918</span> Numerical Simulations on Feasibility of Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taiki%20Baba">Taiki Baba</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither%20quantization" title=" random dither quantization"> random dither quantization</a> </p> <a href="https://publications.waset.org/abstracts/78538/numerical-simulations-on-feasibility-of-stochastic-model-predictive-control-for-linear-discrete-time-systems-with-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25917</span> Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, feedback control systems using random dither quantizers have been proposed for linear discrete-time systems. However, the constraints imposed on state and control variables have not yet been taken into account for the design of feedback control systems with random dither quantization. Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial and terminal time. An important advantage of model predictive control is its ability to handle constraints imposed on state and control variables. Based on the model predictive control approach, the objective of this paper is to present a control method that satisfies probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. In other words, this paper provides a method for solving the optimal control problems subject to probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither" title=" random dither"> random dither</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a> </p> <a href="https://publications.waset.org/abstracts/63970/stochastic-model-predictive-control-for-linear-discrete-time-systems-with-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63970.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25916</span> Synthesis of a Model Predictive Controller for Artificial Pancreas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20El%20Hachimi">Mohamed El Hachimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhakim%20Ballouk"> Abdelhakim Ballouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilyas%20%20Khelafa"> Ilyas Khelafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelaziz%20Mouhou"> Abdelaziz Mouhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Type 1 diabetes occurs when beta cells are destroyed by the body's own immune system. Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an Artificial Pancreas (AP). Method: In this paper, we present a new formulation of the cost function for a Model Predictive Control (MPC) utilizing a technic which accelerates the speed of control of the AP and tackles the nonlinearity of the control problem via asymmetric objective functions. Finding: The finding of this work consists in a new Model Predictive Control algorithm that leads to good performances like decreasing the time of hyperglycaemia and avoiding hypoglycaemia. Conclusion: These performances are validated under in silico trials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20pancreas" title="artificial pancreas">artificial pancreas</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20algorithm" title=" control algorithm"> control algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical%20control" title=" biomedical control"> biomedical control</a>, <a href="https://publications.waset.org/abstracts/search?q=MPC" title=" MPC"> MPC</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function" title=" objective function"> objective function</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinearity" title=" nonlinearity"> nonlinearity</a> </p> <a href="https://publications.waset.org/abstracts/69505/synthesis-of-a-model-predictive-controller-for-artificial-pancreas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69505.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">307</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25915</span> Online Robust Model Predictive Control for Linear Fractional Transformation Systems Using Linear Matrix Inequalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peyman%20Sindareh%20Esfahani">Peyman Sindareh Esfahani</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeffery%20Kurt%20Pieper"> Jeffery Kurt Pieper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the <em>l</em><sub>2</sub>-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20fractional%20transformation" title="linear fractional transformation">linear fractional transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20matrix%20inequality" title=" linear matrix inequality"> linear matrix inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20model%20predictive%20control" title=" robust model predictive control"> robust model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20feedback%20control" title=" state feedback control"> state feedback control</a> </p> <a href="https://publications.waset.org/abstracts/69466/online-robust-model-predictive-control-for-linear-fractional-transformation-systems-using-linear-matrix-inequalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69466.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25914</span> Nonlinear Model Predictive Control of Water Quality in Drinking Water Distribution Systems with DBPs Objetives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mingyu%20Xie">Mingyu Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Mietek%20Brdys"> Mietek Brdys</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper develops a non-linear model predictive control (NMPC) of water quality in drinking water distribution systems (DWDS) based on the advanced non-linear quality dynamics model including disinfections by-products (DBPs). A special attention is paid to the analysis of an impact of the flow trajectories prescribed by an upper control level of the recently developed two-time scale architecture of an integrated quality and quantity control in DWDS. The new quality controller is to operate within this architecture in the fast time scale as the lower level quality controller. The controller performance is validated by a comprehensive simulation study based on an example case study DWDS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20control%20structure" title=" hierarchical control structure"> hierarchical control structure</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20with%20DBPs%20objectives" title=" water quality with DBPs objectives"> water quality with DBPs objectives</a> </p> <a href="https://publications.waset.org/abstracts/32624/nonlinear-model-predictive-control-of-water-quality-in-drinking-water-distribution-systems-with-dbps-objetives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32624.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25913</span> Robust Control of Cyber-Physical System under Cyber Attacks Based on Invariant Tubes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bruno%20Vili%C4%87%20Belina">Bruno Vili膰 Belina</a>, <a href="https://publications.waset.org/abstracts/search?q=Jadranko%20Matu%C5%A1ko"> Jadranko Matu拧ko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid development of cyber-physical systems significantly influences modern control systems introducing a whole new range of applications of control systems but also putting them under new challenges to ensure their resiliency to possible cyber attacks, either in the form of data integrity attacks or deception attacks. This paper presents a model predictive approach to the control of cyber-physical systems robust to cyber attacks. We assume that a cyber attack can be modelled as an additive disturbance that acts in the measuring channel. For such a system, we designed a tube-based predictive controller based. The performance of the designed controller has been verified in Matlab/Simulink environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20systems" title="control systems">control systems</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20attacks" title=" cyber attacks"> cyber attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=resiliency" title=" resiliency"> resiliency</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=tube%20based%20model%20predictive%20control" title=" tube based model predictive control"> tube based model predictive control</a> </p> <a href="https://publications.waset.org/abstracts/169652/robust-control-of-cyber-physical-system-under-cyber-attacks-based-on-invariant-tubes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169652.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">67</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25912</span> Supply Air Pressure Control of HVAC System Using MPC Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Javid">P. Javid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Aeenmehr"> A. Aeenmehr</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Taghavifar"> J. Taghavifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, supply air pressure of HVAC system has been modeled with second-order transfer function plus dead-time. In HVAC system, the desired input has step changes, and the output of proposed control system should be able to follow the input reference, so the idea of using model based predictive control is proceeded and designed in this paper. The closed loop control system is implemented in MATLAB software and the simulation results are provided. The simulation results show that the model based predictive control is able to control the plant properly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20conditioning%20system" title="air conditioning system">air conditioning system</a>, <a href="https://publications.waset.org/abstracts/search?q=GPC" title=" GPC"> GPC</a>, <a href="https://publications.waset.org/abstracts/search?q=dead%20time" title=" dead time"> dead time</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20supply%20control" title=" air supply control"> air supply control</a> </p> <a href="https://publications.waset.org/abstracts/4103/supply-air-pressure-control-of-hvac-system-using-mpc-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4103.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">527</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25911</span> Stability of Stochastic Model Predictive Control for Schr枚dinger Equation with Finite Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent technological advance has prompted significant interest in developing the control theory of quantum systems. Following the increasing interest in the control of quantum dynamics, this paper examines the control problem of Schrödinger equation because quantum dynamics is basically governed by Schrödinger equation. From the practical point of view, stochastic disturbances cannot be avoided in the implementation of control method for quantum systems. Thus, we consider here the robust stabilization problem of Schrödinger equation against stochastic disturbances. In this paper, we adopt model predictive control method in which control performance over a finite future is optimized with a performance index that has a moving initial and terminal time. The objective of this study is to derive the stability criterion for model predictive control of Schrödinger equation under stochastic disturbances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20systems" title=" quantum systems"> quantum systems</a>, <a href="https://publications.waset.org/abstracts/search?q=stabilization" title=" stabilization"> stabilization</a> </p> <a href="https://publications.waset.org/abstracts/62500/stability-of-stochastic-model-predictive-control-for-schrodinger-equation-with-finite-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62500.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">460</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25910</span> A Model Predictive Control Based Virtual Active Power Filter Using V2G Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Zolfaghari">Mahdi Zolfaghari</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Hossein%20Hosseinian"> Seyed Hossein Hosseinian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Askarian%20Abyaneh"> Hossein Askarian Abyaneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehrdad%20Abedi"> Mehrdad Abedi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a virtual active power filter (VAPF) using vehicle to grid (V2G) technology to maintain power quality requirements. The optimal discrete operation of the power converter of electric vehicle (EV) is based on recognizing desired switching states using the model predictive control (MPC) algorithm. A fast dynamic response, lower total harmonic distortion (THD) and good reference tracking performance are realized through the presented control strategy. The simulation results using MATLAB/Simulink validate the effectiveness of the scheme in improving power quality as well as good dynamic response in power transferring capability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicle" title="electric vehicle">electric vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20quality" title=" power quality"> power quality</a>, <a href="https://publications.waset.org/abstracts/search?q=V2G%20technology" title=" V2G technology"> V2G technology</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20active%20power%20filter" title=" virtual active power filter"> virtual active power filter</a> </p> <a href="https://publications.waset.org/abstracts/70930/a-model-predictive-control-based-virtual-active-power-filter-using-v2g-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70930.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">430</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25909</span> Collision Avoidance Based on Model Predictive Control for Nonlinear Octocopter Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Do%C4%9Fan%20Y%C4%B1ld%C4%B1z">Do臒an Y谋ld谋z</a>, <a href="https://publications.waset.org/abstracts/search?q=Aydan%20M%C3%BC%C5%9Ferref%20Erkmen"> Aydan M眉艧erref Erkmen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The controller of the octocopter is mostly based on the PID controller. For complex maneuvers, PID controllers have limited performance capability like in collision avoidance. When an octocopter needs avoidance from an obstacle, it must instantly show an agile maneuver. Also, this kind of maneuver is affected severely by the nonlinear characteristic of octocopter. When these kinds of limitations are considered, the situation is highly challenging for the PID controller. In the proposed study, these challenges are tried to minimize by using the model predictive controller (MPC) for collision avoidance with a nonlinear octocopter model. The aim is to show that MPC-based collision avoidance has the capability to deal with fast varying conditions in case of obstacle detection and diminish the nonlinear effects of octocopter with varying disturbances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20octocopter%20model" title=" nonlinear octocopter model"> nonlinear octocopter model</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20avoidance" title=" collision avoidance"> collision avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20detection" title=" obstacle detection"> obstacle detection</a> </p> <a href="https://publications.waset.org/abstracts/150063/collision-avoidance-based-on-model-predictive-control-for-nonlinear-octocopter-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150063.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25908</span> MPC of Single Phase Inverter for PV System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a model predictive control (MPC) of a utility interactive (UI) single phase inverter (SPI) for a photovoltaic (PV) system at residential/distribution level. The proposed model uses single-phase phase locked loop (PLL) to synchronize SPI with the grid and performs MPC control in a dq reference frame. SPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a full bridge (FB) voltage source inverter (VSI). No PI regulators to tune and carrier and modulating waves are required to produce switching sequence. Instead, the operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a three kW PV system at the input of UI-SPI in Matlab/Simulink. Implementation and results demonstrate simplicity and accuracy, as well as reliability of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phase%20locked%20loop" title="phase locked loop">phase locked loop</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20source%20inverter" title=" voltage source inverter"> voltage source inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20phase%20inverter" title=" single phase inverter"> single phase inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab%2FSimulink" title=" Matlab/Simulink"> Matlab/Simulink</a> </p> <a href="https://publications.waset.org/abstracts/16006/mpc-of-single-phase-inverter-for-pv-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16006.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">532</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25907</span> Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) Control of Quadcopters: A Comparative Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anel%20Hasi%C4%87">Anel Hasi膰</a>, <a href="https://publications.waset.org/abstracts/search?q=Naser%20Prlja%C4%8Da"> Naser Prlja膷a</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the domain of autonomous or piloted flights, the accurate control of quadrotor trajectories is of paramount significance for large numbers of tasks. These adaptable aerial platforms find applications that span from high-precision aerial photography and surveillance to demanding search and rescue missions. Among the fundamental challenges confronting quadrotor operation is the demand for accurate following of desired flight paths. To address this control challenge, among others, two celebrated well-established control strategies have emerged as noteworthy contenders: Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control. In this work, we focus on the extensive examination of MPC and PID control techniques by using comprehensive simulation studies in MATLAB/Simulink. Intensive simulation results demonstrate the performance of the studied control algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title="MATLAB">MATLAB</a>, <a href="https://publications.waset.org/abstracts/search?q=MPC" title=" MPC"> MPC</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a>, <a href="https://publications.waset.org/abstracts/search?q=quadcopter" title=" quadcopter"> quadcopter</a>, <a href="https://publications.waset.org/abstracts/search?q=simulink" title=" simulink"> simulink</a> </p> <a href="https://publications.waset.org/abstracts/186321/model-predictive-control-mpc-and-proportional-integral-derivative-pid-control-of-quadcopters-a-comparative-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25906</span> Sensor Fault-Tolerant Model Predictive Control for Linear Parameter Varying Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yushuai%20Wang">Yushuai Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Xu"> Feng Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Junbo%20Tan"> Junbo Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xueqian%20Wang"> Xueqian Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Liang"> Bin Liang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20parameter%20varying" title=" linear parameter varying"> linear parameter varying</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=set%20theory" title=" set theory"> set theory</a> </p> <a href="https://publications.waset.org/abstracts/134234/sensor-fault-tolerant-model-predictive-control-for-linear-parameter-varying-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134234.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">252</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25905</span> Predictive Functional Control with Disturbance Observer for Tendon-Driven Balloon Actuator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun-ya%20Nagase">Jun-ya Nagase</a>, <a href="https://publications.waset.org/abstracts/search?q=Toshiyuki%20Satoh"> Toshiyuki Satoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Norihiko%20Saga"> Norihiko Saga</a>, <a href="https://publications.waset.org/abstracts/search?q=Koichi%20Suzumori"> Koichi Suzumori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, Japanese society has been aging, engendering a labour shortage of young workers. Robots are therefore expected to perform tasks such as rehabilitation, nursing elderly people, and day-to-day work support for elderly people. The pneumatic balloon actuator is a rubber artificial muscle developed for use in a robot hand in such environments. This actuator has a long stroke, and a high power-to-weight ratio compared with the present pneumatic artificial muscle. Moreover, the dynamic characteristics of this actuator resemble those of human muscle. This study evaluated characteristics of force control of balloon actuator using a predictive functional control (PFC) system with disturbance observer. The predictive functional control is a model-based predictive control (MPC) scheme that predicts the future outputs of the actual plants over the prediction horizon and computes the control effort over the control horizon at every sampling instance. For this study, a 1-link finger system using a pneumatic balloon actuator is developed. Then experiments of PFC control with disturbance observer are performed. These experiments demonstrate the feasibility of its control of a pneumatic balloon actuator for a robot hand. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disturbance%20observer" title="disturbance observer">disturbance observer</a>, <a href="https://publications.waset.org/abstracts/search?q=pneumatic%20balloon" title=" pneumatic balloon"> pneumatic balloon</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20functional%20control" title=" predictive functional control"> predictive functional control</a>, <a href="https://publications.waset.org/abstracts/search?q=rubber%20artificial%20muscle" title=" rubber artificial muscle"> rubber artificial muscle</a> </p> <a href="https://publications.waset.org/abstracts/4030/predictive-functional-control-with-disturbance-observer-for-tendon-driven-balloon-actuator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">453</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25904</span> Experimental Implementation of Model Predictive Control for Permanent Magnet Synchronous Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelsalam%20A.%20Ahmed">Abdelsalam A. Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fast speed drives for Permanent Magnet Synchronous Motor (PMSM) is a crucial performance for the electric traction systems. In this paper, PMSM is drived with a Model-based Predictive Control (MPC) technique. Fast speed tracking is achieved through optimization of the DC source utilization using MPC. The technique is based on predicting the optimum voltage vector applied to the driver. Control technique is investigated by comparing to the cascaded PI control based on Space Vector Pulse Width Modulation (SVPWM). MPC and SVPWM-based FOC are implemented with the TMS320F2812 DSP and its power driver circuits. The designed MPC for a PMSM drive is experimentally validated on a laboratory test bench. The performances are compared with those obtained by a conventional PI-based system in order to highlight the improvements, especially regarding speed tracking response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=permanent%20magnet%20synchronous%20motor" title="permanent magnet synchronous motor">permanent magnet synchronous motor</a>, <a href="https://publications.waset.org/abstracts/search?q=model-based%20predictive%20control" title=" model-based predictive control"> model-based predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=DC%20source%20utilization" title=" DC source utilization"> DC source utilization</a>, <a href="https://publications.waset.org/abstracts/search?q=cascaded%20PI%20control" title=" cascaded PI control"> cascaded PI control</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20vector%20pulse%20width%20modulation" title=" space vector pulse width modulation"> space vector pulse width modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=TMS320F2812%20DSP" title=" TMS320F2812 DSP"> TMS320F2812 DSP</a> </p> <a href="https://publications.waset.org/abstracts/26429/experimental-implementation-of-model-predictive-control-for-permanent-magnet-synchronous-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26429.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">644</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25903</span> MP-SMC-I Method for Slip Suppression of Electric Vehicles under Braking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tohru%20Kawabe">Tohru Kawabe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new SMC (Sliding Mode Control) method with MP (Model Predictive Control) integral action for the slip suppression of EV (Electric Vehicle) under braking is proposed. The proposed method introduce the integral term with standard SMC gain , where the integral gain is optimized for each control period by the MPC algorithms. The aim of this method is to improve the safety and the stability of EVs under braking by controlling the wheel slip ratio. There also include numerical simulation results to demonstrate the effectiveness of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode%20control" title="sliding mode control">sliding mode control</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=integral%20action" title=" integral action"> integral action</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicle" title=" electric vehicle"> electric vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=slip%20suppression" title=" slip suppression"> slip suppression</a> </p> <a href="https://publications.waset.org/abstracts/28617/mp-smc-i-method-for-slip-suppression-of-electric-vehicles-under-braking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28617.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">561</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25902</span> Transfer Function Model-Based Predictive Control for Nuclear Core Power Control in PUSPATI TRIGA Reactor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Sabri%20Minhat">Mohd Sabri Minhat</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Adilla%20Mohd%20Subha"> Nurul Adilla Mohd Subha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 1MWth PUSPATI TRIGA Reactor (RTP) in Malaysia Nuclear Agency has been operating more than 35 years. The existing core power control is using conventional controller known as Feedback Control Algorithm (FCA). It is technically challenging to keep the core power output always stable and operating within acceptable error bands for the safety demand of the RTP. Currently, the system could be considered unsatisfactory with power tracking performance, yet there is still significant room for improvement. Hence, a new design core power control is very important to improve the current performance in tracking and regulating reactor power by controlling the movement of control rods that suit the demand of highly sensitive of nuclear reactor power control. In this paper, the proposed Model Predictive Control (MPC) law was applied to control the core power. The model for core power control was based on mathematical models of the reactor core, MPC, and control rods selection algorithm. The mathematical models of the reactor core were based on point kinetics model, thermal hydraulic models, and reactivity models. The proposed MPC was presented in a transfer function model of the reactor core according to perturbations theory. The transfer function model-based predictive control (TFMPC) was developed to design the core power control with predictions based on a T-filter towards the real-time implementation of MPC on hardware. This paper introduces the sensitivity functions for TFMPC feedback loop to reduce the impact on the input actuation signal and demonstrates the behaviour of TFMPC in term of disturbance and noise rejections. The comparisons of both tracking and regulating performance between the conventional controller and TFMPC were made using MATLAB and analysed. In conclusion, the proposed TFMPC has satisfactory performance in tracking and regulating core power for controlling nuclear reactor with high reliability and safety. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=core%20power%20control" title="core power control">core power control</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=PUSPATI%20TRIGA%20reactor" title=" PUSPATI TRIGA reactor"> PUSPATI TRIGA reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=TFMPC" title=" TFMPC"> TFMPC</a> </p> <a href="https://publications.waset.org/abstracts/93326/transfer-function-model-based-predictive-control-for-nuclear-core-power-control-in-puspati-triga-reactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93326.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">241</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25901</span> Optimizing Microgrid Operations: A Framework of Adaptive Model Predictive Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ruben%20Lopez-Rodriguez">Ruben Lopez-Rodriguez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a microgrid, diverse energy sources (both renewable and non-renewable) are combined with energy storage units to form a localized power system. Microgrids function as independent entities, capable of meeting the energy needs of specific areas or communities. This paper introduces a Model Predictive Control (MPC) approach tailored for grid-connected microgrids, aiming to optimize their operation. The formulation employs Mixed-Integer Programming (MIP) to find optimal trajectories. This entails the fulfillment of continuous and binary constraints, all while accounting for commutations between various operating conditions such as storage unit charge/discharge, import/export from/towards the main grid, as well as asset connection/disconnection. To validate the proposed approach, a microgrid case study is conducted, and the simulation results are compared with those obtained using a rule-based strategy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microgrids" title="microgrids">microgrids</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20logical%20dynamical%20systems" title=" mixed logical dynamical systems"> mixed logical dynamical systems</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-integer%20optimization" title=" mixed-integer optimization"> mixed-integer optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a> </p> <a href="https://publications.waset.org/abstracts/185228/optimizing-microgrid-operations-a-framework-of-adaptive-model-predictive-control" class="btn btn-primary btn-sm">Procedia</a> <a 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