Adaptive Iterative Learning Control Mechanism for Nonlinear Systems subject to High-Order Internal Model

Abstract

This technical note addresses an adaptive iterative learning control (AILC) problem for nonlinear dynamical systems with partially unknown iteration-varying parameter. Referring to the scheme of state-space, an AILC effort is presented for randomly varying reference tracking together with initial shift problem in iteration domain. Furthermore, the AILC technique is extended to systems with several parameters in discussion. A simulation example confirms the validity of the proposed method.