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

This article considers the tracking control of unknown nonlinear nonaffifine repetitive discrete-time multi-input multi-output systems. Two data-driven iterative learning control (ILC) schemes are designed based on two equivalent dynamic linearization data models of an unknown ideal learning controller, which exists theoretically in the iteration domain. The two control schemes provide ways of selecting learning controllers based on the complexity of the controlled nonlinear systems. The learning control gain matrixes of the two learning controllers are optimized through the steepest descent method using only the measured input-output data of the nonlinear systems. The proposed ILC approaches are pure data-driven since no model information of the controlled systems is involved. The stability and convergence of the proposed ILC approaches are rigorously analyzed under reasonable conditions. Numerical simulation and an experiment based on a Gantry-type linear motor drive system are conducted to verify the effectiveness of the proposed data-driven ILC approaches.Cyprus Research Promotion Foundation © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. X. Yu, Z. Hou, M. M. Polycarpou and L. Duan, "Data-Driven Iterative Learning Control for Nonlinear Discrete-Time MIMO Systems," in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1136-1148, March 2021, doi: 10.1109/TNNLS.2020.2980588