Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach

被引：1

作者：

Aarnoudse, Leontine ^{[1
]}

Kon, Johan ^{[1
]}

Classens, Koen ^{[1
]}

van Meer, Max ^{[1
]}

Poot, Maurice ^{[1
]}

Tacx, Paul ^{[1
]}

Strijbosch, Nard ^{[2
]}

Oomen, Tom ^{[1
,3
]}

机构：

[1] Eindhoven Univ Technol, Dept Mech Engn Control Syst Technol, Eindhoven, Netherlands

[2] IBS Precis Engn, Eindhoven, Netherlands

[3] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands

来源：

2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC) | 2022年

关键词：

DESIGN; ILC;

D O I：

10.1109/CDC51059.2022.9993408

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration-and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.

引用

页码：1485 / 1490

页数：6

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