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

Data-driven gradient-based point-to-point iterative learning control for non-linear systems
Data-driven gradient-based point-to-point iterative learning control for non-linear systems
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.
0924-090X
Huo, Benyan
21ab083c-b6cb-4b46-bc5e-e73bf8875bce
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Liu, Yanghong
352f8b6c-7e39-4898-a75b-54258a5d1f90
Huo, Benyan
21ab083c-b6cb-4b46-bc5e-e73bf8875bce
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Liu, Yanghong
352f8b6c-7e39-4898-a75b-54258a5d1f90

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

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.

Text
Data-driven Gradient-based Point-to-Point - Accepted Manuscript
Restricted to Repository staff only until 26 July 2021.
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Accepted/In Press date: 26 July 2020

Identifiers

Local EPrints ID: 442871
URI: http://eprints.soton.ac.uk/id/eprint/442871
ISSN: 0924-090X
PURE UUID: 4d4276b8-a949-427c-90d1-6bb09afbd3e2

Catalogue record

Date deposited: 30 Jul 2020 16:30
Last modified: 08 Oct 2020 16:38

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