Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm

Zhinan Peng, Yiyi Zhao, Jiangping Hu, Bijoy Kumar Ghosh

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Herein, a novel adaptive dynamic programming (ADP) algorithm is developed to solve the optimal tracking control problem of discrete-time multi-agent systems. Compared to the classical policy iteration ADP algorithm with two components, policy evaluation, and policy improvement, a two-stage policy iteration algorithm is proposed to obtain the iterative control laws and the iterative performance index functions. The proposed algorithm contains a sub-iteration procedure to calculate the iterative performance index functions at the policy evaluation. The convergence proof for the iterative performance index functions and the iterative control laws are provided. Subsequently, the stability of the closed-loop error system is also provided. Further, an actor-critic neural network (NN) is used to approximate both the iterative control laws and the iterative performance index functions. The actor-critic NN can implement the developed algorithm online without knowledge of the system dynamics. Finally, simulation results are provided to illustrate the performance of our method.

Original languageEnglish
Pages (from-to)189-202
Number of pages14
JournalInformation Sciences
Volume481
DOIs
StatePublished - May 2019

Keywords

  • Actor-critic networks
  • Data-driven algorithm
  • Multi-agent systems
  • Optimal tracking control
  • Two-stage policy iteration

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