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Distributed iterative learning control for constrained consensus tracking problem

Distributed iterative learning control for constrained consensus tracking problem
Distributed iterative learning control for constrained consensus tracking problem
High precision consensus tracking of networked systems working repetitively has found important applications in various areas. To achieve the high precision tracking, iterative learning control (ILC) has recently been applied. This paper considers the constrained consensus tracking problem in ILC design. We develop a novel constrained ILC algorithm based on the successive projection framework. The resulting algorithm guarantees the satisfaction of the constraints and has appealing convergence properties: when perfect consensus tracking is possible, the tracking error norm converges monotonically to zero; otherwise, it converges monotonically to a ‘best fit’ solution. The proposed algorithm can be applied to both homogeneous and heterogeneous systems, as well as non-minimum phase systems, which is desirable in practice. Furthermore, we provide a distributed implementation for the proposed ILC algorithm using the idea of the alternating direction method of multipliers, allowing the proposed algorithm to be applied to large scale networked systems using only local information. Convergence properties of the algorithm are analysed rigorously and numerical examples are given to demonstrate its effectiveness
3745-3750
IEEE
Chen, B.
c57720bd-1de9-4f03-9f30-f740d9efe876
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Geng, H.
44295491-053d-4da7-bad8-b674e36e1875
Chen, B.
c57720bd-1de9-4f03-9f30-f740d9efe876
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Geng, H.
44295491-053d-4da7-bad8-b674e36e1875

Chen, B., Chu, B. and Geng, H. (2020) Distributed iterative learning control for constrained consensus tracking problem. In Proceedings of the IEEE Conference on Decision and Control. IEEE. pp. 3745-3750 . (doi:10.1109/CDC42340.2020.9303810).

Record type: Conference or Workshop Item (Paper)

Abstract

High precision consensus tracking of networked systems working repetitively has found important applications in various areas. To achieve the high precision tracking, iterative learning control (ILC) has recently been applied. This paper considers the constrained consensus tracking problem in ILC design. We develop a novel constrained ILC algorithm based on the successive projection framework. The resulting algorithm guarantees the satisfaction of the constraints and has appealing convergence properties: when perfect consensus tracking is possible, the tracking error norm converges monotonically to zero; otherwise, it converges monotonically to a ‘best fit’ solution. The proposed algorithm can be applied to both homogeneous and heterogeneous systems, as well as non-minimum phase systems, which is desirable in practice. Furthermore, we provide a distributed implementation for the proposed ILC algorithm using the idea of the alternating direction method of multipliers, allowing the proposed algorithm to be applied to large scale networked systems using only local information. Convergence properties of the algorithm are analysed rigorously and numerical examples are given to demonstrate its effectiveness

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More information

Published date: 2020
Venue - Dates: 59th IEEE Conference on Decision and Control, CDC 2020, , Virtual, Jeju Island, Korea, Republic of, 2020-12-14 - 2020-12-18

Identifiers

Local EPrints ID: 472410
URI: http://eprints.soton.ac.uk/id/eprint/472410
PURE UUID: 29ff4b00-550e-41a8-b5e1-59aef62c5461
ORCID for B. Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 05 Dec 2022 17:36
Last modified: 17 Mar 2024 03:28

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Contributors

Author: B. Chen
Author: B. Chu ORCID iD
Author: H. Geng

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