Method for predicting the lost input of iterative learning control systems over AWGN channels

Abstract

This paper researches the method for predicting the lost input of iterative learning control (ILC) systems over additive white Gaussian noise (AWGN) channels. It directly takes into account a proportional ILC controller, which guarantees the convergence of objects under ideal communication conditions. By employing prior information on ILC controllers and AWGN channels, a state space model is built. According to this model and the innovation analysis theory, a predictor is developed for actuators to calculate the input transmitted by ILC controllers but lost in AWGN channels. Compared with the iteration compensation in existing literature, the proposed prediction compensation simultaneously guarantees the convergence accuracy and speed of ILC systems over AWGN channels. Numerical simulation demonstrates the usefulness of the developed prediction method.