Decentralised collaborative iterative learning control for multi-agent systems point-to-point channel tracking
Decentralised collaborative iterative learning control for multi-agent systems point-to-point channel tracking
Application of learning to collaborative tracking control of multi-agent systems has addressed a wealth of problems across transportation, manufacturing, rescue, aerospace and medical care area. Iterative Learning Control (ILC) algorithms
have been proposed to address synergistic objectives in general optimisation problems, achieving a transparent balance between convergence speed, tracking error and robustness.
Chen, Shangcheng
695035a1-a03d-4539-8ae5-68b7fafbbfb9
Freeman, Chris
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Chen, Shangcheng
695035a1-a03d-4539-8ae5-68b7fafbbfb9
Freeman, Chris
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chen, Shangcheng and Freeman, Chris
(2024)
Decentralised collaborative iterative learning control for multi-agent systems point-to-point channel tracking.
2024 American Control Conference, Westin Harbour Castle, Toronto, Canada.
10 - 12 Jul 2024.
6 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Application of learning to collaborative tracking control of multi-agent systems has addressed a wealth of problems across transportation, manufacturing, rescue, aerospace and medical care area. Iterative Learning Control (ILC) algorithms
have been proposed to address synergistic objectives in general optimisation problems, achieving a transparent balance between convergence speed, tracking error and robustness.
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More information
Accepted/In Press date: 24 January 2024
Venue - Dates:
2024 American Control Conference, Westin Harbour Castle, Toronto, Canada, 2024-07-10 - 2024-07-12
Identifiers
Local EPrints ID: 486783
URI: http://eprints.soton.ac.uk/id/eprint/486783
PURE UUID: c0fbc09d-836c-433a-b710-28e69252bb95
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Date deposited: 06 Feb 2024 17:38
Last modified: 11 Dec 2024 02:39
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Contributors
Author:
Shangcheng Chen
Author:
Chris Freeman
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