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Model-free gradient iterative learning control for non-linear systems

Model-free gradient iterative learning control for non-linear systems
Model-free gradient iterative learning control for non-linear systems
Iterative learning control (ILC) is a well-established approach to precision tracking for systems that perform a repeated task. Gradient-based update laws are amongst the most widely applied in practice due to their attractive robustness properties. However, they are limited by requiring a model of the system dynamics to be identified. This paper shows how gradient ILC can be extended for use with a general class of nonlinear systems, and additionally how the update can be generated using an extra experiment conducted between trials. This 'model-free' algorithm extends previous work for linear systems, and is illustrated by a nonlinear
rehabilitation application requiring accurate control of human upper-limb movement.
iterative learning control, non-linear systems, stroke rehabilitation
2405-8963
304-309
Huo, Benyan
21ab083c-b6cb-4b46-bc5e-e73bf8875bce
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Liu, Yanhong
c4b4a3da-3e3b-4cd0-8d54-2c3e40cfa4ea
Huo, Benyan
21ab083c-b6cb-4b46-bc5e-e73bf8875bce
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Liu, Yanhong
c4b4a3da-3e3b-4cd0-8d54-2c3e40cfa4ea

Huo, Benyan, Freeman, Christopher and Liu, Yanhong (2020) Model-free gradient iterative learning control for non-linear systems. IFAC-PapersOnLine, 52 (29), 304-309. (doi:10.1016/j.ifacol.2019.12.667).

Record type: Article

Abstract

Iterative learning control (ILC) is a well-established approach to precision tracking for systems that perform a repeated task. Gradient-based update laws are amongst the most widely applied in practice due to their attractive robustness properties. However, they are limited by requiring a model of the system dynamics to be identified. This paper shows how gradient ILC can be extended for use with a general class of nonlinear systems, and additionally how the update can be generated using an extra experiment conducted between trials. This 'model-free' algorithm extends previous work for linear systems, and is illustrated by a nonlinear
rehabilitation application requiring accurate control of human upper-limb movement.

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Accepted/In Press date: 29 October 2019
e-pub ahead of print date: 4 December 2019
Published date: 15 January 2020
Venue - Dates: 13th IFAC Workshop on Adaptive and Learning Control Systems, , Winchester, United Kingdom, 2019-12-04 - 2019-12-06
Keywords: iterative learning control, non-linear systems, stroke rehabilitation
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Identifiers

Local EPrints ID: 436762
URI: http://eprints.soton.ac.uk/id/eprint/436762
DOI: doi:10.1016/j.ifacol.2019.12.667
ISSN: 2405-8963
PURE UUID: 5915b64f-c106-48d4-871d-2022bc54b316

Catalogue record

Date deposited: 03 Jan 2020 17:30
Last modified: 16 Mar 2024 07:04

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Contributors

Author: Benyan Huo
Author: Christopher Freeman
Author: Yanhong Liu

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