A Dynamic Linearized Optimal Iterative Learning Control with Constraints and Its Application to High-Speed Trains

This paper considers the constraint problem of input constraint and controller parameter for nonlinear repetitive high-speed trains. To address this problem, a data-driven optimal iterative learning control method is proposed based on controller dynamic linearization. In particular, the constrained...

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Bibliographic Details
Published inData Driven Control and Learning Systems Conference (Online) pp. 798 - 802
Main Authors Yan, Zhenlin, Yu, Xian, Zhou, Yafei, Wu, Zongze
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.05.2025
Subjects
Accuracy
Constrained Optimization
Control systems
Convergence
Data-driven Control
Estimation
Iterative learning control
Learning systems
Least mean squares methods
Nonlinear Repetitive High-speed Trains
Numerical models
Optimal Iterative Learning Control
Optimization
Regularized Least Square Method
Robustness
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ISSN2767-9861
DOI10.1109/DDCLS66240.2025.11065544

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Summary:This paper considers the constraint problem of input constraint and controller parameter for nonlinear repetitive high-speed trains. To address this problem, a data-driven optimal iterative learning control method is proposed based on controller dynamic linearization. In particular, the constrained optimization problem is transformed into an unconstrained optimization problem by introducing the Lagrange method of linear quadratic convex form. Then, the adaptive learning estimation of the controller parameter is obtained through the regularized least square method with analytic suboptimal solution. Finally, the simulations on a numerical example and high-speed train demonstrate that the proposed method has good convergence and tracking accuracy while satisfying constraints.
ISSN:2767-9861
DOI:10.1109/DDCLS66240.2025.11065544