Robust point-to-point iterative learning control with trial-varying initial conditions

• Published in: IET control theory & applications Vol. 14; no. 19; pp. 3344 - 3350
• Main Authors: Tao, Hongfeng, Li, Jian, Chen, Yiyang, Stojanovic, Vladimir, Yang, Huizhong
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 21.12.2020
• Subjects: Brief Paper; control algorithms; convex programming; high‐performance technique; iterative learning control; iterative methods; learning systems; norm‐optimal ILC; point‐to‐point tasks; point‐to‐point tracking task; robust control; robust ILC algorithm; tracking performance; trial‐varying initial conditions; trial‐varying parameters; worst‐case norm‐optimal problem; iterative methods; worst-case norm-optimal problem; point-to-point tasks; convex programming; control algorithms; robust ILC algorithm; learning systems; high-performance technique; point-to-point tracking task; iterative learning control; trial-varying parameters; robust control; trial-varying initial conditions; tracking performance; norm-optimal ILC
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2020.0557

Iterative learning control (ILC) is a high-performance technique for repeated control tasks with design postulates on a fixed reference profile and identical initial conditions. However, the tracking performance is only critical at few points in point-to-point tasks, and their initial conditions are usually trial-varying within a certain range in practice, which essentially degrades the performance of conventional ILC algorithms. Therefore, this study reformulates the ILC problem setup for point-to-point tasks and considers the effort of trial-varying initial conditions in algorithm design. To reduce the tracking error, it proposes a worst-case norm-optimal problem and reformulates it into a convex optimisation problem using the Lagrange dual approach. In this sense, a robust ILC algorithm is derived based on iteratively solving this problem. The study also shows that the proposed robust ILC is equivalent to conventional norm-optimal ILC with trial-varying parameters. A numerical simulation case study is conducted to compare the performance of this algorithm with that of other control algorithms while performing a given point-to-point tracking task. The results reveal its efficiency for the specific task and robustness against trial-varying initial conditions.