Reinforcement learning‐based adaptive optimal tracking algorithm for Markov jump systems with partial unknown dynamics

Y. Tu; H. Fang; H. Wang; K. Shi; S. He

doi:10.1002/oca.2903

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Reinforcement learning‐based adaptive optimal tracking algorithm for Markov jump systems with partial unknown dynamics

Journal article

Peer reviewed

Reinforcement learning‐based adaptive optimal tracking algorithm for Markov jump systems with partial unknown dynamics

Y. Tu, H. Fang, H. Wang, K. Shi and S. He

Optimal Control Applications and Methods, Vol.43(5), pp.1435-1449

2022

DOI: https://doi.org/10.1002/oca.2903

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Abstract

In this article, a novel method is proposed to solve the adaptive optimal tracking algorithm for a class of Markov jump systems. First, the augmented system with the tracking signal is built under the decoupling Markov jump systems and it is proved that the selected performance index satisfies the algebraic Riccati equation which can be solved by policy iteration schemes. Then, a reinforcement learning (RL) algorithm is used to solve the coupled algebraic Riccati equations by using partial knowledge of system dynamics. The convergence of the partial model-free integral RL iteration algorithm is also proved. Finally, a simulation example is given to show the better tracking effectiveness and accuracy of the online iteration algorithm comparing with the offline one.

Details

Title: Reinforcement learning‐based adaptive optimal tracking algorithm for Markov jump systems with partial unknown dynamics
Authors/Creators: Y. Tu (Author/Creator) - Anhui University
H. Fang (Author/Creator) - Chinese University of Hong Kong
H. Wang (Author/Creator) - Murdoch University
K. Shi (Author/Creator) - Chengdu University
S. He (Author/Creator) - Anhui University
Publication Details: Optimal Control Applications and Methods, Vol.43(5), pp.1435-1449
Publisher: John Wiley & Sons Ltd
Identifiers: 991005542727307891
Murdoch Affiliation: School of Engineering and Energy
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.116 Robotics; 4.116.862 Reinforcement Learning
Web Of Science research areas: Automation & Control Systems; Mathematics, Applied; Operations Research & Management Science
ESI research areas: Engineering