Iterative Learning Control (ILC) is a technique of tracking control aiming at improving tracking performance for systems that work in a repetitive mode. ILC is a simple and effective control and can progressively reduce tracking errors and improve system performance from iteration to iteration. In this paper, we first classify the ILC schemes into three categories: offline learning scheme, online learning scheme, and online-offline learning scheme. In each scheme, P-type, D-type, PD-type, and switching gain learning control are discussed. The corresponding convergence conditions for each type of ILCs are presented. Then, different ILCs are applied to control a general nonlinear system with noise and disturbance. After that, various ILC schemes are tested under different test conditions to compare the effectiveness and robustness. It is demonstrated that the online-offline type ILCs can obtain the best tracking performance, and the switching gain learning control can provide the fastest convergence speed.

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