Optimisation-based iterative learning control for distributed consensus tracking
Optimisation-based iterative learning control for distributed consensus tracking
This paper addresses high-performance consensus tracking of repetitively operating networked dynamical systems using an iterative learning control (ILC) algorithm. It circumvents the need for precise model information in traditional methods and guarantees the high-performance by the predictive framework with a novel performance index that takes into account both current and future performance. The proposed algorithm ensures geometric convergence of the tracking error norm to zero and can be applied to both heterogeneous and non-minimum-phase systems. A distributed implementation of the algorithm is developed using the Alternating Direction Method of Multipliers, with detailed convergence analysis and numerical examples confirming its effectiveness.
119-124
Zhang, Yueqing
ab6a3071-e2b6-431e-8b8d-b14e00ade9f6
Chen, Bin
c57720bd-1de9-4f03-9f30-f740d9efe876
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Shu, Zhan
ea5dc18c-d375-4db0-bbcc-dd0229f3a1cb
22 May 2024
Zhang, Yueqing
ab6a3071-e2b6-431e-8b8d-b14e00ade9f6
Chen, Bin
c57720bd-1de9-4f03-9f30-f740d9efe876
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Shu, Zhan
ea5dc18c-d375-4db0-bbcc-dd0229f3a1cb
Zhang, Yueqing, Chen, Bin, Chu, Bing and Shu, Zhan
(2024)
Optimisation-based iterative learning control for distributed consensus tracking.
In 2024 UKACC 14th International Conference on Control, CONTROL 2024.
IEEE.
.
(doi:10.1109/CONTROL60310.2024.10532118).
Record type:
Conference or Workshop Item
(Paper)
Abstract
This paper addresses high-performance consensus tracking of repetitively operating networked dynamical systems using an iterative learning control (ILC) algorithm. It circumvents the need for precise model information in traditional methods and guarantees the high-performance by the predictive framework with a novel performance index that takes into account both current and future performance. The proposed algorithm ensures geometric convergence of the tracking error norm to zero and can be applied to both heterogeneous and non-minimum-phase systems. A distributed implementation of the algorithm is developed using the Alternating Direction Method of Multipliers, with detailed convergence analysis and numerical examples confirming its effectiveness.
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Published date: 22 May 2024
Venue - Dates:
14th UKACC International Conference on Control, CONTROL 2024, , Winchester, United Kingdom, 2024-04-10 - 2024-04-12
Identifiers
Local EPrints ID: 499505
URI: http://eprints.soton.ac.uk/id/eprint/499505
PURE UUID: 8a5f8d34-1ca9-4a35-bdff-a7384b887def
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Date deposited: 21 Mar 2025 17:55
Last modified: 11 Sep 2025 03:39
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