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

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...

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Bibliographic Details
Published inData 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
LanguageEnglish
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
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ISSN2767-9861
DOI10.1109/DDCLS61622.2024.10606907

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Summary: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.
ISSN:2767-9861
DOI:10.1109/DDCLS61622.2024.10606907