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Nonzero-sum games using actor-critic neural networks: A dynamic event-triggered adaptive dynamic programming.

Authors :
Shen, Hao
Li, Ziwei
Wang, Jing
Cao, Jinde
Source :
Information Sciences. Mar2024, Vol. 662, pN.PAG-N.PAG. 1p.
Publication Year :
2024

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00200255
Volume :
662
Database :
Academic Search Index
Journal :
Information Sciences
Publication Type :
Periodical
Accession number :
175456713
Full Text :
https://doi.org/10.1016/j.ins.2024.120236