A Data-Driven Iterative Learning Control Framework Based on Controller Dynamic Linearization

Abstract

A novel data-driven iterative learning control (ILC) framework is proposed in this paper for a class of unknown nonlinear repetitive discrete-time systems by applying the iterative dynamic linearization (IDL) technique, which is an extension of dynamic linearization (DL) in iteration domain. The prototype DL is applied for unknown nonlinear systems and then extended for unknown ideal nonlinear controllers. By applying the idea of the IDL for unknown general iterative learning controller, a novel learning controller with a learning gain in iteration domain is constructed, and the learning gain is automatically tuned with only requirement of the measured I/O data of the controlled nonlinear repetitive systems. The main contribution of this work is that most of the existing ILC controllers can be considered as a special case of this method. The convergence of the proposed data-driven ILC framework is guaranteed through rigorous theoretical analysis and the effectiveness is validated with simulation results.