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

Huo, Benyan, Freeman, Christopher and Liu, Yanghong (2020) Data-driven gradient-based point-to-point iterative learning control for non-linear systems. Nonlinear Dynamics, 102, 269–283.

Record type: Article

Abstract

Iterative learning control (ILC) is a well established methodology which has proven 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 focusses 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.

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Data-driven Gradient-based Point-to-Point - Accepted Manuscript

Available under License University of Southampton Accepted Manuscript Licence.

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