Work

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

Year: 2024

Type: article

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.

Source: Mathematics

DOI: 10.3390/math12162499

Authors: Weiqing Yang, Yuyang Zhou, Yong Zhang, Yan Ren

Institutions: Inner Mongolia University of Science and Technology, Edinburgh Napier University

Language: English

Topic: Adaptive Dynamic Programming Control

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funders: Inner Mongolia University of Science and Technology, National Natural Science Foundation of China, Inner Mongolia University, Natural Science Foundation of Inner Mongolia