Adaptive Optimal Control for Multi-player Systems with Completely Unknown Dynamic

Zhihao Zhang, Jing Shi*, Jin Zhang, Chen Peng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, we propose model-free adaptive dynamic programming based on identifier-critic neural networks for a multi-player system where the dynamics are completely unknown. First, we develop an identifier network that identifies a multi-player system with completely unknown dynamics. Then, based on a critical neural network, each player is approximated to minimize the cost function for each player resulting in the best controller for each player. Subsequently, the Lyapunov method is used to prove that the NN estimation errors and the system states are uniformly ultimate boundedness (UUB). Finally, the effectiveness of the proposed method is verified by a numerical simulation.

Original languageEnglish
Title of host publication2023 42nd Chinese Control Conference, CCC 2023
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789887581543
StatePublished - 2023
Externally publishedYes
Event42nd Chinese Control Conference, CCC 2023 - Tianjin, China
Duration: 24 Jul 202326 Jul 2023

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Conference42nd Chinese Control Conference, CCC 2023


  • Adaptive dynamic programming
  • Identifier-Critic
  • Multi-player systems


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