Data-Driven Iterative Learning Control for Nonlinear Discrete-Time MIMO Systems

This article considers the tracking control of unknown nonlinear nonaffine repetitive discrete-time multi-input multi-output systems. Two data-driven iterative learning (ILC) schemes are designed based on two equivalent dynamic linearization data models an ideal controller, which exists theoretically in iteration domain. The provide ways selecting controllers complexity controlled gain matrixes optimized through steepest descent method using only measured input-output proposed ILC approaches pure since no model information systems is involved. stability and convergence rigorously analyzed under reasonable conditions. Numerical simulation experiment a Gantry-type linear motor drive system conducted to verify effectiveness approaches.