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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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Accepted/In Press date: 14 July 2017
e-pub ahead of print date: 18 October 2017
Published date: 18 October 2017
Venue - Dates: IFAC 2017 World Congress, The 20th World Congress of the International Federation of Automatic Control, Toulouse, France, 2017-07-09 - 2017-07-14
Keywords: iterative learning control, constraint handling
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 404113
URI: http://eprints.soton.ac.uk/id/eprint/404113
ISSN: 2405-8963
PURE UUID: 695271ed-517d-444c-a043-0238fa9197ba
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: 22 Dec 2016 10:15
Last modified: 17 Mar 2024 03:28

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Contributors

Author: Yiyang Chen ORCID iD
Author: Bing Chu ORCID iD
Author: Christopher T. Freeman

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