A Trust-Region Method for Data-Driven Iterative Learning Control of Nonlinear Systems

被引：1

作者：

Wang, Jia ^{[1
]}

Hemelhof, Leander ^{[1
]}

Markovsky, Ivan ^{[2
,3
]}

Patrinos, Panagiotis ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Elect Engn ESAT STADIUS, B-3001 Leuven, Belgium

[2] Gran Capita, Int Ctr Numer Methods Engn, Barcelona 08034, Spain

[3] Catalan Inst Res & Adv Studies, Barcelona 08010, Spain

来源：

IEEE CONTROL SYSTEMS LETTERS | 2024年 / 8卷

关键词：

Polynomials; Interpolation; Vectors; Jacobian matrices; Trajectory; Process control; Periodic structures; Data-driven control; iterative learning control; nonlinear system control; derivative-free optimization; DESIGN;

D O I：

10.1109/LCSYS.2024.3417805

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter employs a derivative-free trust-region method to solve the norm-optimal iterative learning control problem for nonlinear systems with unknown dynamics. The iteration process is composed by two kinds of trials: main and additional trials. The tracking error is reduced in each main trial, and the additional trials explore the nonlinear dynamics around the main trial input. Then the trust-region subproblem is constructed based on the additional trial data, and solved to generate the next main trial input. The convergence of the tracking error is proved under mild assumptions. Our method is illustrated in simulations https://github.com/JiaaaWang/TRILC.

引用

页码：1847 / 1852

页数：6

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