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Generalized norm optimal iterative learning control: constraint handling

Generalized norm optimal iterative learning control: constraint handling
Generalized norm optimal iterative learning control: constraint handling
This paper proposes a novel control methodology to incorporate constraint handling within generalized iterative learning control (ILC), an overarching methodology which includes intermediate point and sub-interval tracking as special cases. The constrained generalized ILC design objective is first described, and then the design problem is formulated into a successive projection framework. This framework yields a constrained generalized ILC algorithm which embeds system input and output constraints. Convergence analysis of the algorithm is performed and supported by rigorous proofs. The algorithm is verified using a gantry robot experimental platform, whose results reveal its practical efficacy and robustness against plant uncertainty.
iterative learning control, constraint handling
2405-8963
13396-13401
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Chen, Yiyang, Chu, Bing and Freeman, Christopher T. (2017) Generalized norm optimal iterative learning control: constraint handling. IFAC-PapersOnLine, 50 (1), 13396-13401. (doi:10.1016/j.ifacol.2017.08.2275).

Record type: Article

Abstract

This paper proposes a novel control methodology to incorporate constraint handling within generalized iterative learning control (ILC), an overarching methodology which includes intermediate point and sub-interval tracking as special cases. The constrained generalized ILC design objective is first described, and then the design problem is formulated into a successive projection framework. This framework yields a constrained generalized ILC algorithm which embeds system input and output constraints. Convergence analysis of the algorithm is performed and supported by rigorous proofs. The algorithm is verified using a gantry robot experimental platform, whose results reveal its practical efficacy and robustness against plant uncertainty.

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e-pub ahead of print date: 18 October 2017
Keywords: iterative learning control, constraint handling

Identifiers

Local EPrints ID: 429886
URI: https://eprints.soton.ac.uk/id/eprint/429886
ISSN: 2405-8963
PURE UUID: 16a4694f-1908-49ee-8c26-75d7dc8375ec
ORCID for Yiyang Chen: ORCID iD orcid.org/0000-0001-9960-9040
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 08 Apr 2019 16:30
Last modified: 09 Apr 2019 00:32

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Contributors

Author: Yiyang Chen ORCID iD
Author: Bing Chu ORCID iD

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