Adaptive iterative learning control for switched nonlinear continuous-time systems

ABSTRACT

In this paper, an adaptive iterative learning control (ILC) method is proposed for switched nonlinear continuous-time systems with time-varying parametric uncertainties. First, an iterative learning controller is constructed with a state feedback term in the time domain and an adaptive learning term in the iteration domain. Then a switched nonlinear continuous-discrete two-dimensional (2D) system is built to describe the adaptive ILC system. Multiple 2D Lyapunov functions-based analysis ensures that the 2D system is exponentially stable, and the tracking error will converge to zero in the iteration domain. The design method of the iterative learning controller is obtained by solving a linear matrix inequality. Finally, the efficacy of the proposed controller is demonstrated by the simulation results.

KEYWORDS:

• Iterative learning control
• switched systems
• 2D systems
• average dwell time

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities [grant number 2018B01914].

Notes on contributors

Zhen Shao

Zhen Shao was born in Jiangsu Province, China, in 1990. She received her Ph.D degree in Control Theory and Control Engineering at Nanjing University of Science and Technology (NUST), Nanjing, China, in 2017. She is currently lecturer at Hohai University. Her current research interests include repetitive control, ILC, nonlinear systems and switched systems.

Zhengrong Xiang

Zhengrong Xiang received his Ph.D. degree in Control Theory and Control Engineering at Nanjing University of Science and Technology (NUST), Nanjing, China, in 1998. Since 1998 he has been faculty member and he is currently full professor at NUST. He was appointed as Lecturer in 1998 and Associate Professor in 2001 at NUST. He is a member of the IEEE, member of the Chinese Association for Artificial Intelligence. His main research interests include switched systems, nonlinear control, robust control, and networked control systems.