Feedback Feedforward Iterative Learning Control for Networked Nonlinear System Under Iteratively Variable Trial Lengths and Data Dropouts

Abstract

This paper proposed a feedback feedforward Iterative Learning Control (ILC) law for nonlinear system with iteratively variable trial lengths under a networked systems structure, where the both sensor and actuator occurs random data lost separately. The feedforward ILC part includes the calculated input signal, actual input signal, and the modified tracking error of last iteration. Some tracking signal would be lost at last iteration because of the iterative varying trial lengths. In order to offset the missing signal of last trial, the tracking error of present trial is adopted by feedback control part. It is established that the convergence relied on the feedforward control gain merely, while the rate of convergence is also expedited by the feedback control component. When the initial state expectation equals to the reference one, it is established that the tracking error expectation can be controlled to zero. With an illustrative simulation, the effectiveness of the developed algorithm can be demonstrated.