Work

RL‐Based H∞ Optimal Tracking Control for Markov Jump Singularly Perturbed Systems

Year: 2026

Type: article

Abstract: ABSTRACT In this article, the reinforcement learning (RL)‐based optimal tracking control for a class of discrete‐time Markov jump singularly perturbed systems (MJSPSs) is investigated under the conditions of unknown system dynamics and external disturbances. First, an augmented system is constructed by integrating the reference model with the system dynamics, upon which a zero‐sum game performance index is established. Consequently, the corresponding coupled game algebraic Riccati equation (CGARE) and the ‐function Bellman equation are derived. Subsequently, a model‐free ‐learning algorithm is proposed to approximate the optimal solution of the CGARE by utilizing collected input‐state data, eliminating the reliance on precise system models. Finally, the effectiveness of the proposed method is validated through a tunnel diode circuit simulation, where stable tracking performance is achieved in the presence of external disturbances.

Source: Optimal Control Applications and Methods

DOI: 10.1002/oca.70153

Authors: Xuewen Shi, Luyu Liu, Wenhai Qi, Jinde Cao, Jun Cheng, Kaibo Shi

Institutions: Qufu Normal University, Southeast University, Guangxi Normal University, Chengdu University

Language: English

Topic: Adaptive Dynamic Programming Control

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province

Award: G5451752748