Reliable Control of Nonlinear System with Input Saturation by Adaptive Iterative Learning Control

Abstract

In this paper, reliable control strategy is studied for nonlinear system with input saturation by adaptive iterative learning control. The system dynamic function is described by a class of nonlinearly parameterized functions with input saturation and actuator faults. In order to address nonlinearity of system, input saturation and the actuator fault term, we design the adaptive iterative learning reliable controller (AILRC), which is a feedback P-type ILC controller. Based on the constructed composite energy function (CEF) and some necessary assumptions, the convergence analysis is given, which shows that the system tracking error converges to zero when the iteration number tends to infinity. Finally, simulation is given to illustrate the correctness of the proposed AILRC.