Robust Optimization-Based Iterative Learning Control for Nonlinear Systems With Nonrepetitive Uncertainties

• Published in: IEEE/CAA journal of automatica sinica Vol. 8; no. 5; pp. 1001 - 1014
• Main Authors: Meng, Deyuan, Zhang, Jingyao
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.05.2021; Seventh Research Division, Beihang University (BUAA), Beijing 100191; School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing 100191, China
• Subjects: Adaptive iterative learning control (ILC); Adaptive systems; Control systems; Convergence; Design methodology; Design optimization; Injection molding; Iterative learning control; Learning; Nonlinear control; Nonlinear systems; nonlinear time-varying system; Nonlinearity; Optimization; Parameter estimation; Robust control; robust convergence; substochastic matrix; Time-varying systems; Trajectory analysis; Uncertainty; Adaptive iterative learning control (ILC); substochastic matrix; robust convergence; nonlinear time-varying system
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2021.1003973

This paper aims to solve the robust iterative learning control (ILC) problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties. A new optimization-based method is proposed to design and analyze adaptive ILC, for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices. It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC, where the boundedness of system trajectories and estimated parameters can be ensured, regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties. Two simulation tests, especially implemented for an injection molding process, demonstrate the effectiveness of our robust optimization-based ILC results.