Neural network-based adaptive optimal containment control for non-affine nonlinear multi-agent systems within an identifier-actor-critic framework
This paper addresses the adaptive optimal containment control issue for non-affine nonlinear multi-agent systems in the presence of periodic disturbances. To deal with the disturbed internal dynamics, a fourier series expansion-neural networks-based adaptive identifier is designed for each follower,...
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| Published in | Journal of the Franklin Institute Vol. 360; no. 12; pp. 8118 - 8143 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.08.2023
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| Online Access | Get full text |
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| Abstract | This paper addresses the adaptive optimal containment control issue for non-affine nonlinear multi-agent systems in the presence of periodic disturbances. To deal with the disturbed internal dynamics, a fourier series expansion-neural networks-based adaptive identifier is designed for each follower, such that the restrictions posed on the system dynamics are released. Then,an adaptive dynamic programming technique is adopted to acquire the optimized virtual and actual controllers under a simplified actor-critic architecture, where the critic aims to appraise control performance and the actor aims to perform control task. Note that the above updating laws are constructed by the negative gradient of a designed function, which is constructed on the basis of the partial derivative of Hamilton-Jacobi-Bellman equation. Finally, simulation results are provided to show the applicability and effectiveness of the containment control scheme. |
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| AbstractList | This paper addresses the adaptive optimal containment control issue for non-affine nonlinear multi-agent systems in the presence of periodic disturbances. To deal with the disturbed internal dynamics, a fourier series expansion-neural networks-based adaptive identifier is designed for each follower, such that the restrictions posed on the system dynamics are released. Then,an adaptive dynamic programming technique is adopted to acquire the optimized virtual and actual controllers under a simplified actor-critic architecture, where the critic aims to appraise control performance and the actor aims to perform control task. Note that the above updating laws are constructed by the negative gradient of a designed function, which is constructed on the basis of the partial derivative of Hamilton-Jacobi-Bellman equation. Finally, simulation results are provided to show the applicability and effectiveness of the containment control scheme. |
| Author | Niu, Ben Alharbi, Khalid H. Zhao, Xudong Zhao, Yanwei Zong, Guangdeng |
| Author_xml | – sequence: 1 givenname: Yanwei surname: Zhao fullname: Zhao, Yanwei email: yanweizhao2021@163.com organization: College of Control Science and Engineering, Bohai University, Jinzhou 121013, China – sequence: 2 givenname: Ben surname: Niu fullname: Niu, Ben email: niubensdnu@163.com organization: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China – sequence: 3 givenname: Guangdeng orcidid: 0000-0001-6498-5580 surname: Zong fullname: Zong, Guangdeng email: lovelyletian@gmail.com organization: School of Control Science and Engineering, Tiangong University, Tianjin 300387, China – sequence: 4 givenname: Xudong surname: Zhao fullname: Zhao, Xudong email: xdzhaohit@gmail.com organization: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China – sequence: 5 givenname: Khalid H. surname: Alharbi fullname: Alharbi, Khalid H. email: khalharbi@kau.edu.sa organization: Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia |
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| Title | Neural network-based adaptive optimal containment control for non-affine nonlinear multi-agent systems within an identifier-actor-critic framework |
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