Effective tracker design based on iterative learning control methodology with input constraint for a class of unknown interconnected large-scale sampled-data nonlinear systems

Abstract

This paper proposes the decentralized iterative learning control (ILC) for a class of unknown sampled-data interconnected large-scale nonlinear with a closed-loop decoupling property via the off-line observer/Kalman filter identification (OKID) method. First, the OKID method not only is utilized to determine decentralized appropriate (low-) order discrete-time linear models for the class of unknown interconnected large-scale sampled-data systems by using known input-output sampled data but also to overcome the effect of modeling error on the identified linear model of each subsystem. For the tracking purpose, a norm-optimal ILC (NOILC) scheme is embedded to the decentralized models, and the constrained ILC problem is formulated in a successive projection framework. To reduce unwanted learning cycles, the digital-redesign linear quadratic tracker with the high-gain property is proposed to assign the initial control input of ILC. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed methodologies.