Robust iterative learning control for iteration- and time-varying disturbance rejection

ABSTRACT

Iterative learning control (ILC) is an effective strategy to deal with repetitive tasks and has been widely applied in industrial systems. Many methods have been proposed to improving the performance of ILC system against iteration-invariant disturbances. While iteration-varying disturbances, which has more practical meaning, do not get enough researches. An observer is designed to estimate the system states and the total disturbances which include the system uncertainties and external disturbances. Furthermore, an iterative algorithm is given to estimate separately the disturbances from input and non-input channels. Then robust D-type ILC with disturbances compensation is proposed to improve the performance of systems with iteration-varying and time-varying disturbances. The convergence of proposed robust ILC system is proved, and the control parameters design is guided. Finally, simulation and comparison with other method are carried out to demonstrate the efficiency of the proposed method.

KEYWORDS:

• Iterative learning control
• iteration-varying disturbances
• observer
• disturbance rejection

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Jing Wang http://orcid.org/0000-0002-6847-8452

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 61573050, 61973023], Beijing Natural Science Foundation 4202052 and Fundamental Research Funds for the Central Universities of China [grant number XK1802-4].

Notes on contributors

Chengyuan Tan

Chengyuan Tan received the Bachelor’s degree in automation control and Master’s degree in Control Science and Engineering from Beijing University of Chemical Technology, in 2016 and 2019, respectively. His research is mainly related to iterative learning control, robust control.

Sen Wang

Sen Wang received the Bachelor’s degree in automation control, Master’s degree in Control Science and Engineering from Henan Polytechnic University, in 2016 and 2019, respectively. He is currently pursuing his Ph.D. degree in Control Science and Engineering with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. His current research interests include data-driven control, iterative learning control, network control systems.

Jing Wang

Jing Wang received the B.S. degree in industry automation, the Ph.D. degree in control theory and control engineering from the Northeastern University, in 1994 and 1998, respectively. She is a Professor with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. She was a visiting Professor at University of Delaware, USA in 2014. Her research interests include advanced control, process monitoring, fault detection and diagnosis and their applications.