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.
|Title of host publication||Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022|
|Editors||Wenxing Fu, Mancang Gu, Yifeng Niu|
|Number of pages||10|
|Publication status||Published - 2023|
|Event||International Conference on Autonomous Unmanned Systems, ICAUS 2022 - Xi'an, China|
Duration: 23 Sep 2022 → 25 Sep 2022
|Name||Lecture Notes in Electrical Engineering|
|Conference||International Conference on Autonomous Unmanned Systems, ICAUS 2022|
|Period||23/09/22 → 25/09/22|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care 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