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Reinforcement learning-based composite suboptimal control for Markov jump singularly perturbed systems with unknown dynamics
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  • Jiacheng Wu,
  • Wenqian Li,
  • Yun Wang,
  • Hao Shen
Jiacheng Wu
Anhui University of Technology

Corresponding Author:[email protected]

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Wenqian Li
Anhui University of Technology
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Yun Wang
Anhui University of Technology
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Hao Shen
Anhui University of Technology
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Abstract

In this article, a model-free parallel reinforcement learning method is proposed to solve the suboptimal control problem for the Markov jump singularly perturbed systems. First, since fast and slow dynamics coexist in Markov jump singularly perturbed systems, it may lead to ill-conditioned numerical problems during the controller design process. Therefore, the original system can be decomposed into independent subsystems at different time-scales by employing the reduced order method. Besides, a model-based parallel algorithm is designed to obtain the optimal controllers of the fast and slow subsystems respectively. Moreover, within the framework of reinforcement learning, the composite controller of the Markov jump singularly perturbed systems can be obtained without system dynamics. Finally, a numerical example is introduced to prove the effectiveness of proposed algorithms.
28 Jul 2023Submitted to Optimal Control, Applications and Methods
28 Jul 2023Submission Checks Completed
28 Jul 2023Assigned to Editor
28 Jul 2023Review(s) Completed, Editorial Evaluation Pending
18 Aug 2023Reviewer(s) Assigned