A Direct Reinforcement Learning Approach for Nonautonomous Thermoacoustic Generator

Author:

Do Trong Tan12,Tran Duc Chuyen3ORCID,Le Duy Tung1,Dao Phuong Nam1ORCID

Affiliation:

1. School of Electrical Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

2. Center for Micro Electronics and Information Technology, Hanoi, Vietnam

3. University of Economics-Technology for Industries, Hanoi, Vietnam

Abstract

For nonautonomous nonlinear systems, the optimal control design is affected by the terms of partial derivative. If a reinforcement learning (RL) strategy is developed to approximate the optimal control scheme in nonautonomous nonlinear systems, then the closed control system might be unstabilizing. Therefore, in this article, the approach of direct RL law for a nonautonomous thermoacoustic generator (TAG) is investigated. We establish the mathematical model of TAG by partial differential equations (PDEs) and then transforming them into time varying nonlinear systems. The direct RL technique with Newton–Leibniz formula is implemented to consider the partial derivative term from classical policy iteration (PI) method by modifying the computation using data collection between the two sampling times. Finally, several simulation studies with some comparisons are conducted to validate the theoretical analyses.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Policy iteration for H control of polynomial time‐varying systems;IET Control Theory & Applications;2024-04-12

2. Cyber-Physical Power and Energy Systems with Wireless Sensor Networks: A Systematic Review;Journal of Electrical Engineering & Technology;2023-04-03

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