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A decentralised iterative learning control framework for collaborative tracking

A decentralised iterative learning control framework for collaborative tracking
A decentralised iterative learning control framework for collaborative tracking
Collaborative tracking control involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. Decentralised iterative learning control schemes have demonstrated highly accurate collaborative tracking by using past experience gained over repeated attempts at the task. However they impose highly restrictive constraints on the system dynamics, and their reliance on inverse dynamics has degraded their robustness to model uncertainty.
This paper proposes the first general decentralised iterative learning framework to address this problem, thereby enabling a wide range of existing iterative learning control methodologies to be applied in a decentralised manner to collaborative subsystems. This framework is illustrated through the derivation of a variety of new decentralised iterative learning control algorithms which balance
collaborative tracking performance with optimisation of a general objective function. The framework is illustrated by application to wearable stroke rehabilitation technology in which each subsystem is a muscle artificially activated by electrical stimulation. These verify the framework’s simplified design and reduced hardware and communication overheads.
Collaborative control, Iterative learning control, Stroke rehabilitation
0957-4158
Chen, Shangcheng
ad9127e5-deb9-48be-bc8b-c8719f2c023a
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chen, Shangcheng
ad9127e5-deb9-48be-bc8b-c8719f2c023a
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Chen, Shangcheng and Freeman, Christopher (2020) A decentralised iterative learning control framework for collaborative tracking. Mechatronics, 72, [102465]. (doi:10.1016/j.mechatronics.2020.102465).

Record type: Article

Abstract

Collaborative tracking control involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. Decentralised iterative learning control schemes have demonstrated highly accurate collaborative tracking by using past experience gained over repeated attempts at the task. However they impose highly restrictive constraints on the system dynamics, and their reliance on inverse dynamics has degraded their robustness to model uncertainty.
This paper proposes the first general decentralised iterative learning framework to address this problem, thereby enabling a wide range of existing iterative learning control methodologies to be applied in a decentralised manner to collaborative subsystems. This framework is illustrated through the derivation of a variety of new decentralised iterative learning control algorithms which balance
collaborative tracking performance with optimisation of a general objective function. The framework is illustrated by application to wearable stroke rehabilitation technology in which each subsystem is a muscle artificially activated by electrical stimulation. These verify the framework’s simplified design and reduced hardware and communication overheads.

Text
MechatronicsShangchengV4_R2 - Accepted Manuscript
Restricted to Repository staff only until 27 November 2022.
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More information

Accepted/In Press date: 15 November 2020
e-pub ahead of print date: 27 November 2020
Published date: December 2020
Additional Information: Publisher Copyright: © 2020 Elsevier Ltd Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
Keywords: Collaborative control, Iterative learning control, Stroke rehabilitation

Identifiers

Local EPrints ID: 445286
URI: http://eprints.soton.ac.uk/id/eprint/445286
ISSN: 0957-4158
PURE UUID: cb285873-c75f-4cbb-9088-f1a54f2e7257
ORCID for Shangcheng Chen: ORCID iD orcid.org/0000-0001-8032-2779

Catalogue record

Date deposited: 25 Nov 2021 23:06
Last modified: 10 Jan 2022 02:31

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Contributors

Author: Shangcheng Chen ORCID iD
Author: Christopher Freeman

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