Online Iterative Adaptive Dynamic Programming Approach for Solving the Zero-Sum Game for Nonlinear Continuous-Time Systems with Partially Unknown Dynamics

Bin Fu, Bo Sun, Hang Guo, Tao Yang, Wenxing Fu

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

1 Scopus citations

Abstract

The current study presents an online iterative adaptive dynamic programming approach to resolve the zero-sum game (ZSG) for nonlinear continuous-time (CT) systems containing a partially unknown dynamic. The Hamilton-Jacobian-Issacs (HJI) equation is solved along the state trajectory according to the value function approximation and the policy improvement online. Relaxed dynamic programming is utilized to ensure the algorithm’s convergence. Model and costate networks were established to conduct the method. Computational simulations are performed to present the efficiency of the algorithm.

Original languageEnglish
Title of host publicationProceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022
EditorsWenxing Fu, Mancang Gu, Yifeng Niu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages2833-2842
Number of pages10
ISBN (Print)9789819904785
DOIs
StatePublished - 2023
EventInternational Conference on Autonomous Unmanned Systems, ICAUS 2022 - Xi'an, China
Duration: 23 Sep 202225 Sep 2022

Publication series

NameLecture Notes in Electrical Engineering
Volume1010 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Autonomous Unmanned Systems, ICAUS 2022
Country/TerritoryChina
CityXi'an
Period23/09/2225/09/22

Keywords

  • Approximation dynamic programming
  • Integral reinforcement learning
  • Online learning
  • Value iteration
  • Zero-sum game

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