Optimality and flexibility in Iterative Learning Control for varying tasks. (May 2016)

• Record Type: Journal Article
• Authors: van Zundert, Jurgen; Bolder, Joost; Oomen, Tom
• Is Part Of: Automatica. Volume 67(2016)
• Page Start: 295
• Page End: 302
• Publication Date: 2016-05
• Subjects: Iterative Learning Control -- Basis functions -- Varying reference -- Instrumental variable; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2016.01.026
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: Iterative Learning Control (ILC) can significantly enhance the performance of systems that perform repeating tasks. However, small variations in the performed task may lead to a large performance deterioration. The aim of this paper is to develop a novel ILC approach, by exploiting rational basis functions, that enables performance enhancement through iterative learning while providing flexibility with respect to task variations. The proposed approach involves an iterative optimization procedure after each task, that exploits recent developments in instrumental variable-based system identification. Enhanced performance compared to pre-existing results is proven theoretically and illustrated through simulation examples.