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

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 it...

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Vydáno v:Information sciences Ročník 481; s. 189 - 202
Hlavní autoři: Peng, Zhinan, Zhao, Yiyi, Hu, Jiangping, Ghosh, Bijoy Kumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.05.2019
Témata:
Actor-critic networks
Data-driven algorithm
Multi-agent systems
Optimal tracking control
Two-stage policy iteration
Actor-critic networks
Optimal tracking control
Two-stage policy iteration
Data-driven algorithm
Multi-agent systems
ISSN:0020-0255, 1872-6291
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Shrnutí: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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2018.12.079