Iteration-dependent High-order Internal Model based Iterative Learning Control for Discrete-time Nonlinear Systems with Time-iteration-varying Parameter

Abstract

Abstract In this paper, an adaptive iterative learning control (AILC) scheme is designed for discrete-time nonlinear systems with random initial condition and time-iteration-varying parameter. The time-iteration-varying parameter is generated by a general iteration-varying high-order internal model (HOIM) with iteration-varying order and coefficients, and the parameter updating law is designed based on least square method. Compared with the existing works based on iteration-invariant HOIM with fixed order and coefficients, our work significantly extends the application scope of HOIM-based ILC. Using the designed HOIM based iterative learning controller, the learning convergence in the iteration domain is guaranteed through rigorous theoretical analysis under Lyapunov theory. Moreover, an illustrative example is given to demonstrate the effectiveness of the proposed method.