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Reinforcement Q-Learning for PDF Tracking Control of Stochastic Systems with Unknown Dynamics

Author

Listed:
  • Weiqing Yang

    (School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
    Key Laboratory of Synthetical Automation for Process Industries at Universities of Inner Mongolia Autonomous Region, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Yuyang Zhou

    (School of Computing Engineering and Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK)

  • Yong Zhang

    (School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
    Key Laboratory of Synthetical Automation for Process Industries at Universities of Inner Mongolia Autonomous Region, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Yan Ren

    (School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
    Key Laboratory of Synthetical Automation for Process Industries at Universities of Inner Mongolia Autonomous Region, Inner Mongolia University of Science and Technology, Baotou 014010, China)

Abstract

Tracking control of the output probability density function presents significant challenges, particularly when dealing with unknown system models and multiplicative noise disturbances. To address these challenges, this paper introduces a novel tracking control algorithm based on reinforce-ment Q-learning. Initially, a B-spline model is employed to represent the original system, thereby transforming the control problem into a state weight tracking issue within the B-spline stochastic system model. Moreover, to tackle the challenge of unknown stochastic system dynamics and the presence of multiplicative noise, a model-free reinforcement Q-learning algorithm is employed to solve the control problem. Finally, the proposed algorithm’s effectiveness is validated through comprehensive simulation examples.

Suggested Citation

  • Weiqing Yang & Yuyang Zhou & Yong Zhang & Yan Ren, 2024. "Reinforcement Q-Learning for PDF Tracking Control of Stochastic Systems with Unknown Dynamics," Mathematics, MDPI, vol. 12(16), pages 1-15, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2499-:d:1455446
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