Internal reinforcement adaptive dynamic programming for optimal containment control of unknown continuous-time multi-agent systems

Jiefu Zhang, Zhinan Peng, Jiangping Hu, Yiyi Zhao, Rui Luo, Bijoy Kumar Ghosh

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, a novel control scheme is developed to solve an optimal containment control problem of unknown continuous-time multi-agent systems. Different from traditional adaptive dynamic programming (ADP) algorithms, this paper proposes an internal reinforcement ADP algorithm (IR-ADP), in which the internal reinforcement signals are added in order to facilitate the learning process. Then a distributed containment control law is designed for each agent with the internal reinforcement signal. The convergence of this IR-ADP algorithm and the stability of the closed-loop multi-agent system are analyzed theoretically. For the implementation of the optimal controllers, three neural networks (NNs), namely internal reinforcement NNs, critic NNs and actor NNs, are utilized to approximate the internal reinforcement signals, the performance indices and optimal control laws, respectively. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Original languageEnglish
Pages (from-to)85-95
Number of pages11
JournalNeurocomputing
Volume413
DOIs
StatePublished - Nov 6 2020

Keywords

  • Adaptive dynamic programming
  • Internal reinforcement learning
  • Multi-agent system
  • Neural network
  • Optimal containment control

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