Year: 2025
Type: article
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.
Source: Mathematics
DOI: 10.3390/math13213501
Authors: Xiang-Lai Li, Qiu-Lin Wu
Institutions: Hunan Institute of Engineering, Xiangtan University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4415814747