Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems

This paper focuses on the optimal containment control problem for the nonlinear multiagent systems with partially unknown dynamics via an integral reinforcement learning algorithm. By employing integral reinforcement learning, the requirement of the drift dynamics is relaxed. The integral reinforcem...

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Detalles Bibliográficos
Autores principales: Wu, Qiuye, Wu, Yongheng, Wang, Yonghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955993/
https://www.ncbi.nlm.nih.gov/pubmed/36832588
http://dx.doi.org/10.3390/e25020221
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author Wu, Qiuye
Wu, Yongheng
Wang, Yonghua
author_facet Wu, Qiuye
Wu, Yongheng
Wang, Yonghua
author_sort Wu, Qiuye
collection PubMed
description This paper focuses on the optimal containment control problem for the nonlinear multiagent systems with partially unknown dynamics via an integral reinforcement learning algorithm. By employing integral reinforcement learning, the requirement of the drift dynamics is relaxed. The integral reinforcement learning method is proved to be equivalent to the model-based policy iteration, which guarantees the convergence of the proposed control algorithm. For each follower, the Hamilton–Jacobi–Bellman equation is solved by a single critic neural network with a modified updating law which guarantees the weight error dynamic to be asymptotically stable. Through using input–output data, the approximate optimal containment control protocol of each follower is obtained by applying the critic neural network. The closed-loop containment error system is guaranteed to be stable under the proposed optimal containment control scheme. Simulation results demonstrate the effectiveness of the presented control scheme.
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spelling pubmed-99559932023-02-25 Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems Wu, Qiuye Wu, Yongheng Wang, Yonghua Entropy (Basel) Article This paper focuses on the optimal containment control problem for the nonlinear multiagent systems with partially unknown dynamics via an integral reinforcement learning algorithm. By employing integral reinforcement learning, the requirement of the drift dynamics is relaxed. The integral reinforcement learning method is proved to be equivalent to the model-based policy iteration, which guarantees the convergence of the proposed control algorithm. For each follower, the Hamilton–Jacobi–Bellman equation is solved by a single critic neural network with a modified updating law which guarantees the weight error dynamic to be asymptotically stable. Through using input–output data, the approximate optimal containment control protocol of each follower is obtained by applying the critic neural network. The closed-loop containment error system is guaranteed to be stable under the proposed optimal containment control scheme. Simulation results demonstrate the effectiveness of the presented control scheme. MDPI 2023-01-23 /pmc/articles/PMC9955993/ /pubmed/36832588 http://dx.doi.org/10.3390/e25020221 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wu, Qiuye
Wu, Yongheng
Wang, Yonghua
Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems
title Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems
title_full Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems
title_fullStr Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems
title_full_unstemmed Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems
title_short Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems
title_sort integral reinforcement-learning-based optimal containment control for partially unknown nonlinear multiagent systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955993/
https://www.ncbi.nlm.nih.gov/pubmed/36832588
http://dx.doi.org/10.3390/e25020221
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AT wuyongheng integralreinforcementlearningbasedoptimalcontainmentcontrolforpartiallyunknownnonlinearmultiagentsystems
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