ESO-Based Cyclic Iterative Learning Control for Continuous System with Varying Trials

This paper devises a cyclic iterative learning control (CILC) method based on extended state observer (ESO) to enhance the space-induced periodic disturbances rejection performance as well as uncertainties for continuous system with varying trials. Conventional ILC is based on the strict premise that the system executes the same length of trial for the subsequent trial, which is not appropriate in the majority of occasions. Besides, there exist aperiodic disturbances, uncertainties, and spatial-varying properties. To make the problem tractable, a data-driven ESO-based cyclic iterative learning control approach is proposed and synthesized. The cyclic memory whose data are stored with respect to the angular position rather than time are compressed and the filter compensation of ESO-based CILC is investigated. The reduction of error are guaranteed by iteration under rotating circles. Numerical simulations and comparisons are provided to demonstrate the efficacy and effectiveness of the proposed method.