Nonzero-sum games using actor-critic neural networks: A dynamic event-triggered adaptive dynamic programming
This paper mainly investigates the nonzero-sum games of nonlinear systems with unmatched uncertainty by using actor-critic neural networks. To handle the unmatched components, an auxiliary system with a modified value function is constructed, which transforms the robust stabilization issue into the...
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| Published in: | Information sciences Vol. 662; p. 120236 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.03.2024
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | This paper mainly investigates the nonzero-sum games of nonlinear systems with unmatched uncertainty by using actor-critic neural networks. To handle the unmatched components, an auxiliary system with a modified value function is constructed, which transforms the robust stabilization issue into the optimal control issue. Then, a novel dynamic event-triggering condition is designed to further save bandwidth via introducing a dynamic variable. In addition, the actor-critic algorithm is employed in adaptive dynamic programming to achieve Nash equilibrium, which is tuned together with the control policy. By constructing appropriate Lyapunov functions, a criterion is established to ensure that the considered system is uniformly ultimately bounded. Finally, the effectiveness of the developed strategy is demonstrated by an example. |
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| AbstractList | This paper mainly investigates the nonzero-sum games of nonlinear systems with unmatched uncertainty by using actor-critic neural networks. To handle the unmatched components, an auxiliary system with a modified value function is constructed, which transforms the robust stabilization issue into the optimal control issue. Then, a novel dynamic event-triggering condition is designed to further save bandwidth via introducing a dynamic variable. In addition, the actor-critic algorithm is employed in adaptive dynamic programming to achieve Nash equilibrium, which is tuned together with the control policy. By constructing appropriate Lyapunov functions, a criterion is established to ensure that the considered system is uniformly ultimately bounded. Finally, the effectiveness of the developed strategy is demonstrated by an example. |
| ArticleNumber | 120236 |
| Author | Cao, Jinde Shen, Hao Li, Ziwei Wang, Jing |
| Author_xml | – sequence: 1 givenname: Hao orcidid: 0000-0001-7024-6573 surname: Shen fullname: Shen, Hao email: haoshen@ahut.edu.cn organization: Anhui Provincial Key Laboratory of Power Electronics and Motion Control and School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243002, China – sequence: 2 givenname: Ziwei surname: Li fullname: Li, Ziwei email: ziweilim27@gmail.com organization: Anhui Provincial Key Laboratory of Power Electronics and Motion Control and School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243002, China – sequence: 3 givenname: Jing surname: Wang fullname: Wang, Jing email: jingwang08@126.com organization: Anhui Provincial Key Laboratory of Power Electronics and Motion Control and School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243002, China – sequence: 4 givenname: Jinde surname: Cao fullname: Cao, Jinde email: jdcao@seu.edu.cn organization: The School of Mathematics, Southeast University, Nanjing 210096, China |
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| Keywords | Nonzero-sum games Actor-critic neural networks Adaptive dynamic programming Dynamic event-triggered mechanism |
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| SubjectTerms | Actor-critic neural networks Adaptive dynamic programming Dynamic event-triggered mechanism Nonzero-sum games |
| Title | Nonzero-sum games using actor-critic neural networks: A dynamic event-triggered adaptive dynamic programming |
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