State‐tracking iterative learning control in frequency domain design for improved intersample behavior. (28th November 2022)

• Record Type: Journal Article
• Authors: Ohnishi, Wataru; Strijbosch, Nard; Oomen, Tom
• Other Names: Meng Deyuan guestEditor.; Moore Kevin L. guestEditor.; Chi Ronghu guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 33:Number 7(2023)
• Page Start: 4009
• Page End: 4027
• Publication Date: 2022-11-28
• Subjects: frequency domain design -- intersample behavior -- iterative learning control -- multirate inversion -- system inversion; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.6511
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: Iterative learning control (ILC) yields perfect output‐tracking performance at sampling instances for systems that perform repetitive tasks. The aim of this article is to develop a framework for a state‐tracking ILC that mitigates oscillatory intersample behavior, which is often encountered in output tracking ILC. As a framework for the analysis, the stability of the iterative domain including the robustness filter and the asymptotic signal is formulated. In addition, as a framework for the design, the design method using frequency response data to reduce the modeling effort, the learning filter design based on inversion, and the specific design procedure of the robustness filter are presented. The designed method is successfully applied to a motion system and it is shown that the presented state‐tracking ILC provides better intersample behavior than the standard output‐tracking ILC.