Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems

Author

Yue Fu ; Jun Fu ; Tianyou Chai

Author_Institution

State Key Lab. of Synthetical Autom. for Process Ind., Northeastern Univ., Shenyang, China

Volume

26

Issue

12

fYear

2015

Firstpage

3314

Lastpage

3319

Abstract

In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.

Keywords

PI control; asymptotic stability; closed loop systems; continuous time systems; dynamic programming; game theory; iterative methods; linear systems; uncertain systems; PI scheme; closed-loop system; continuous-time linear systems; continuous-time unknown linear systems; convergence proof; global asymptotic stability; iteration loop; online robust adaptive dynamic programming algorithm; policy iteration scheme; suboptimal property; two-player zero-sum games; uncertainty matching; Approximation algorithms; Closed loop systems; Convergence; Games; Heuristic algorithms; Linear systems; Robustness; Game algebraic Riccati equation (GARE); policy iterations (PIs); robust adaptive dynamic programming (ADP); two-player zero-sum (ZS) games; two-player zero-sum (ZS) games.;

fLanguage

English

Journal_Title

Neural Networks and Learning Systems, IEEE Transactions on

Publisher

ieee

ISSN

2162-237X

Type

jour

DOI

10.1109/TNNLS.2015.2461452

Filename

7312453