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

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
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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
Number of pages10
ISBN (Print)9789819904785
Publication statusPublished - 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


ConferenceInternational Conference on Autonomous Unmanned Systems, ICAUS 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


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

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