Model-Free H/H Predictive Control for Discrete-Time System via Q-Learning

• Published in: Data Driven Control and Learning Systems Conference (Online) pp. 1532 - 1537
• Main Authors: Lin, Yihong, He, Peng, Wan, Haiying, Liu, Zhuangyu, Luan, Xiaoli, Liu, Fei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.05.2024
• Subjects: Approximation algorithms; Discrete-time systems; H2/ H∞ performance index; Heuristic algorithms; Model-free predictive control; Prediction algorithms; Predictive models; Q-learning; Receding horizon optimization; Simulation; Unknown discrete-time systems
• ISSN: 2767-9861
• DOI: 10.1109/DDCLS61622.2024.10606907

This paper presents a model-free H_{2}/H_{\infty} Q-learning predictive control strategy for linear discrete-time systems. To design predictive controller with the system measured states, a policy iteration solution algorithm is employed to approximate the control inputs. Specifically, the developed algorithm is formulated in the form of linear matrix inequalities, designed to stabilize the system with mixed H_{2}/H_{\infty} control performance. Additionally, to improve robust performance under system disturbance variations, we introduce the receding horizon optimization into the Q-learning based predictive control. Finally, simulation results demonstrate the effectiveness of the proposed approach.