Dynamic ILC for Linear Repetitive Processes Based on Different Relative Degrees

Lei Wang, Liangxin Dong, Ruitian Yang () and Yiyang Chen ()
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Lei Wang: School of Automation, Wuxi University, Wuxi 214105, China
Liangxin Dong: School of Automation, Nanjing University of Information Science and Technology, Wuxi 214105, China
Ruitian Yang: School of Automation, Wuxi University, Wuxi 214105, China
Yiyang Chen: School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China

Mathematics, 2022, vol. 10, issue 24, 1-13

Abstract: The current research on iterative learning control focuses on the condition where the system relative degree is equal to 1, while the condition where the system relative degree is equal to 0 or greater than 1 is not considered. Therefore, this paper studies the monotonic convergence of the corresponding dynamic iterative learning controller systematically for discrete linear repetitive processes with different relative degrees. First, a 2D discrete Roesser model of the iterative learning control system is presented by means of 2D systems theory. Then, the monotonic convergence condition of the controlled system is analyzed according to the stability theory of linear repetitive process. Furthermore, the sufficient conditions of the controller existence are given in linear matrix inequality format under different relative degrees, which guarantees the system dynamic performance. Finally, through comparison with static controllers under different relative degrees, the simulation results show that the designed schemes are effective and feasible.

Keywords: linear repetitive process; relative degrees; dynamic iterative learning control; monotonic convergence; linear matrix inequality (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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