Optimal Asymptotic Tracking Control for Nonzero-Sum Differential Game Systems with Unknown Drift Dynamics via Integral Reinforcement Learning

This paper employs an integral reinforcement learning (IRL) method to investigate the optimal tracking control problem (OTCP) for nonlinear nonzero-sum (NZS) differential game systems with unknown drift dynamics. Unlike existing methods, which can only bound the tracking error, the proposed approach...

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Hlavní autoři:	Chonglin Jing, Chaoli Wang, Hongkai Song, Yibo Shi, Longyan Hao
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2024-08-01
Edice:	Mathematics
Témata:	nonzero-sum games optimal asymptotic tracking control integral reinforcement learning neural network
On-line přístup:	https://www.mdpi.com/2227-7390/12/16/2555
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