Variable‐gain PD‐type iterative learning control for a class of nonlinear time‐varying systems

Abstract

AbstractThis paper investigates variable‐gain PD‐type iterative learning control (ILC) for a class of nonlinear time‐varying systems to well balance high‐gain convergence rate and low‐gain noise transmission. Different from the classic PD‐type ILC, the control gains of the proposed method are variable. Each variable‐gain consists of an amplitude‐dependent term and an iteration‐varying term. The amplitude‐dependent terms vary with the amplitudes of tracking error and derivative of tracking error, and the iteration‐varying terms are increasing along the iteration axis. The proposed ILC achieves a faster convergence rate than low‐gain ILC and higher tracking accuracy with limited noise amplification than high‐gain ILC. Moreover, the convergence condition of the proposed method in the presence of external noise is provided. Simulation and experimental results demonstrate the effectiveness of the proposed method.