Data-Driven Optimal Preview Repetitive Control of Linear Discrete-Time Systems

Xiang-Lai Li () and Qiu-Lin Wu
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Xiang-Lai Li: College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411101, China
Qiu-Lin Wu: School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China

Mathematics, 2025, vol. 13, issue 21, 1-12

Abstract: This paper investigates the problem of data-driven optimal preview repetitive control of linear discrete-time systems. Firstly, by integrating prior information into the preview time domain, an augmented state-space system is established. Secondly, the original output tracking problem is mathematically reconstructed and transformed into the optimization problem form of a linear quadratic tracking (LQR). Furthermore, a Q-function-based iterative algorithm is designed to dynamically calculate the optimal tracking control gain based solely on online measurable data. This method has a dual-breakthrough feature: it neither requires prior knowledge of system dynamics nor provides an initial stable controller. Finally, the superiority of the proposed scheme is verified through numerical simulation experiments.

Keywords: data-driven preview repetitive control; discrete-time systems; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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