Data-driven gradient-based point-to-point iterative learning control for nonlinear systems

Abstract

Iterative learning control (ILC) is a well-established methodology which has proved successful in achieving accurate tracking control for repeated tasks. However, the majority of ILC algorithms require a nominal plant model and are sensitive to modelling mismatch. This paper focuses on the class of gradient-based ILC algorithms and proposes a data-driven ILC implementation applicable to a general class of nonlinear systems, in which an explicit model of the plant dynamics is not required. The update of the control signal is generated by an additional experiment executed between ILC trials. The framework is further extended by allowing only specific reference points to be tracked, thereby enabling faster convergence. Necessary convergence conditions and corresponding convergence rates for both approaches are derived theoretically. The proposed data-driven approaches are demonstrated through application to a stroke rehabilitation problem requiring accurate control of nonlinear artificially stimulated muscle dynamics.