Robust Tracking of Nonrepetitive Learning Control Systems With Iteration-Dependent References

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 51; no. 2; pp. 842 - 852
• Main Authors: Meng, Deyuan, Zhang, Jingyao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convergence; Environment models; High-order internal models (HOIM); Iterative learning control; iterative learning control (ILC); Iterative methods; Learning; nonrepetitive systems; Robustness; Task analysis; Tracking control; Trajectory; Uncertainty
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2018.2883383

Typically, iterative learning control (ILC) is applied based on a core hypothesis that the strict repetitiveness of control environment, task, and model should be satisfied by the controlled system. The problem of interest in this paper is: whether and how can ILC robustly work for controlled systems subject to iteration-dependent environments, tasks and models? To successfully solve this problem, an ILC algorithm using a high-order internal model (HOIM) is proposed and convergence conditions are developed. It is shown that HOIM-based ILC both possesses robustness against iteration-dependent uncertainties from initial states, disturbances, and plant models and tracks iteration-dependent references. Also, simulation tests validate the effectiveness of HOIM-based ILC.