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Decentralised collaborative and formation iterative learning control for multi-agent systems

Decentralised collaborative and formation iterative learning control for multi-agent systems
Decentralised collaborative and formation iterative learning control for multi-agent systems
Collaborative tracking and formation control are common approaches in which multiple agents work together to perform a global objective. They are increasingly used in a diverse range of applications, however few controllers simultaneously address both tasks. To improve performance of repeated tasks, Iterative learning control (ILC) has been independently applied to each agents. However, focus has been on centralized structures, and existing solutions typically have limited convergence rates and robustness properties. This paper addresses current limitations by developing a powerful decentralised framework which enables broad classes of ILC algorithm to be derived with well-defined convergence rates, optimal tracking solutions, and transparent robustness properties. The framework is illustrated through derivation of three new ILC updates, inverse, gradient and norm optimal ILC. Convergence analysis for the proposed framework is also given.
0743-1619
IEEE
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 (2019) Decentralised collaborative and formation iterative learning control for multi-agent systems. In 2020 American Control Conference (ACC). IEEE. 6 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Collaborative tracking and formation control are common approaches in which multiple agents work together to perform a global objective. They are increasingly used in a diverse range of applications, however few controllers simultaneously address both tasks. To improve performance of repeated tasks, Iterative learning control (ILC) has been independently applied to each agents. However, focus has been on centralized structures, and existing solutions typically have limited convergence rates and robustness properties. This paper addresses current limitations by developing a powerful decentralised framework which enables broad classes of ILC algorithm to be derived with well-defined convergence rates, optimal tracking solutions, and transparent robustness properties. The framework is illustrated through derivation of three new ILC updates, inverse, gradient and norm optimal ILC. Convergence analysis for the proposed framework is also given.

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Accepted/In Press date: 1 July 2019
e-pub ahead of print date: 1 July 2019
Published date: 29 August 2019
Venue - Dates: 2020 American Control Conference, ACC 2020, 2020-07-01 - 2020-07-03

Identifiers

Local EPrints ID: 442454
URI: http://eprints.soton.ac.uk/id/eprint/442454
ISSN: 0743-1619
PURE UUID: 831e89c9-eb36-43b5-a273-8a9e4ecf0b53
ORCID for Shangcheng Chen: ORCID iD orcid.org/0000-0001-8032-2779

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Date deposited: 15 Jul 2020 16:31
Last modified: 07 Oct 2020 01:31

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