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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Bibliographic Details
Published in:	Information sciences Vol. 662; p. 120236
Main Authors:	Shen, Hao, Li, Ziwei, Wang, Jing, Cao, Jinde
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.03.2024
Subjects:	Actor-critic neural networks Adaptive dynamic programming Dynamic event-triggered mechanism Nonzero-sum games Nonzero-sum games Actor-critic neural networks Adaptive dynamic programming Dynamic event-triggered mechanism
ISSN:	0020-0255, 1872-6291
Online Access:	Get full text
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Summary:	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.
ISSN:	0020-0255 1872-6291
DOI:	10.1016/j.ins.2024.120236