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Q-Learning-Based Multi-Rate Optimal Control for Process Industries

Zhenxing XiaSchool of Information and Control Engineering and Engineering Research Center of Intelligent Control for Underground Space, China University of Mining and Technology, Xuzhou, ChinaMengjie HuSchool of Information and Control Engineering and Engineering Research Center of Intelligent Control for Underground Space, China University of Mining and Technology, Xuzhou, ChinaWei DaiSchool of Information and Control Engineering and Engineering Research Center of Intelligent Control for Underground Space, China University of Mining and Technology, Xuzhou, ChinaHuaicheng YanKey Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, ChinaXiaoping MaSchool of Information and Control Engineering and Engineering Research Center of Intelligent Control for Underground Space, China University of Mining and Technology, Xuzhou, China

2022en

ABI

Annotatsiya

This brief studies the multi-rate optimal control problem for a class of industrial processes, whose controlling rate will be set faster than the sampling rate sometimes. This multi-rate phenomenon makes the accurate modeling of control systems challenging and difficult. In this brief, we present a model-free self-learning control scheme for the real-time solution of this problem, combining the lifting technology and Q-learning. For the asynchronous periods, the lifting system is established first to reconstruct the input and output by stacking the control and sampling signals to a frame period, maintaining the original dynamic information. Then, Q-learning is adopted to learn the optimal control policy with the real-time data and the convergence analysis of the proposed algorithm is derived. In this way, the control actions are executed at a faster rate to obtain the better dynamic performance. Finally, a hardware-in-loop (HIL) simulation study for process industries is carried out, showing that the proposed approach has high tracking and real-time performance.

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Identifikatorlar

DOI: 10.1109/tcsii.2022.3219255

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