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Predictive norm optimal iterative learning control for high-performance formation control problem

Predictive norm optimal iterative learning control for high-performance formation control problem
Predictive norm optimal iterative learning control for high-performance formation control problem
This paper develops a predictive optimisationbased iterative learning control (ILC) strategy for the highperformance formation control problem in networked dynamical systems working repetitively. It avoids the need for exact model information in traditional methods and achieves high performance via a predictive framework incorporating a unique performance index that integrates both immediate and future performance. The proposed framework guarantees geometric convergence of the formation error norm to zero and is capable of handling both heterogeneous and non-minimum phase systems. A distributed implementation of the framework is developed using the Alternating Direction Method of Multipliers to guarantee the framework’s scalability for largescale networks. Rigorous convergence analysis and numerical examples are provided to confirm its effectiveness.
4911-4916
IEEE
Zhang, Yueqing
ab6a3071-e2b6-431e-8b8d-b14e00ade9f6
Chen, Bin
c57720bd-1de9-4f03-9f30-f740d9efe876
Zhang, Yueqing
ab6a3071-e2b6-431e-8b8d-b14e00ade9f6
Chen, Bin
c57720bd-1de9-4f03-9f30-f740d9efe876

Zhang, Yueqing and Chen, Bin (2025) Predictive norm optimal iterative learning control for high-performance formation control problem. In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE. pp. 4911-4916 . (doi:10.1109/CDC56724.2024.10885829).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper develops a predictive optimisationbased iterative learning control (ILC) strategy for the highperformance formation control problem in networked dynamical systems working repetitively. It avoids the need for exact model information in traditional methods and achieves high performance via a predictive framework incorporating a unique performance index that integrates both immediate and future performance. The proposed framework guarantees geometric convergence of the formation error norm to zero and is capable of handling both heterogeneous and non-minimum phase systems. A distributed implementation of the framework is developed using the Alternating Direction Method of Multipliers to guarantee the framework’s scalability for largescale networks. Rigorous convergence analysis and numerical examples are provided to confirm its effectiveness.

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Published date: 26 February 2025

Identifiers

Local EPrints ID: 500050
URI: http://eprints.soton.ac.uk/id/eprint/500050
PURE UUID: e825d227-7141-4f60-83b8-96d94fc3508f
ORCID for Yueqing Zhang: ORCID iD orcid.org/0000-0003-2304-6151

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Date deposited: 14 Apr 2025 16:36
Last modified: 30 Sep 2025 02:19

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Contributors

Author: Yueqing Zhang ORCID iD
Author: Bin Chen

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