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Decentralised collaborative iterative learning control for MIMO multi-agent systems

Decentralised collaborative iterative learning control for MIMO multi-agent systems
Decentralised collaborative iterative learning control for MIMO multi-agent systems
Collaborative tracking control of multi-agent systems (MAS) involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. However, existing, predominately centralised, control structures are sensitive to communication delays and data drop-out leading to inaccurate tracking. Moreover, comparatively little attention has been paid to the case of multiple input, multiple output (MIMO) linear agent systems. Iterative learning control (ILC) has been applied to increase tracking performance using past experience over repeated task attempts, but current ILC research assumes the ‘lifted’ system of each agent is full rank (i.e. each agent can achieve the task independently).
This paper proposes a novel decentralised ILC framework, which can be applied to both full and non-full rank MIMO MAS. This framework provides powerful general conditions to design decentralised ILC laws. It is exemplified by application to derive three new decentralised ILC approaches: inverse, gradient and norm optimal ILC. Convergence and robustness analysis for the proposed framework are also given.
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 iterative learning control for MIMO multi-agent systems. In Proceedings of the IEEE American Control Conference.

Record type: Conference or Workshop Item (Paper)

Abstract

Collaborative tracking control of multi-agent systems (MAS) involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. However, existing, predominately centralised, control structures are sensitive to communication delays and data drop-out leading to inaccurate tracking. Moreover, comparatively little attention has been paid to the case of multiple input, multiple output (MIMO) linear agent systems. Iterative learning control (ILC) has been applied to increase tracking performance using past experience over repeated task attempts, but current ILC research assumes the ‘lifted’ system of each agent is full rank (i.e. each agent can achieve the task independently).
This paper proposes a novel decentralised ILC framework, which can be applied to both full and non-full rank MIMO MAS. This framework provides powerful general conditions to design decentralised ILC laws. It is exemplified by application to derive three new decentralised ILC approaches: inverse, gradient and norm optimal ILC. Convergence and robustness analysis for the proposed framework are also given.

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More information

Published date: 10 July 2019
Venue - Dates: American Control Conference 2018: ACC 2018, Milwaukee, United States, 2018-06-27 - 2018-06-29

Identifiers

Local EPrints ID: 428850
URI: https://eprints.soton.ac.uk/id/eprint/428850
PURE UUID: 2e8a0e55-311f-42f3-b745-2672b7c33c48
ORCID for Shangcheng Chen: ORCID iD orcid.org/0000-0001-8032-2779

Catalogue record

Date deposited: 13 Mar 2019 17:30
Last modified: 15 Mar 2019 01:38

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