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Iterative learning control laws with full dynamics

Iterative learning control laws with full dynamics
Iterative learning control laws with full dynamics
Iterative learning control can be applied to systems that execute the same finite duration task over and over again. The distinguishing feature is the use of information from previous executions to construct the input to the next one in the sequence, including time domain information that would be non-causal in standard control systems. Many algorithms or laws have been developed for an ever increasing range of applications. This paper develops a new law which is fully dynamic, not static, when implemented. Experimental verification results are also given.
6369-6374
IEEE
Hladowski, Lukasz
db41c3fd-6c9e-48e8-81e7-9613072c59b5
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Nowicka, Weronika
e1ac7b8b-e806-4bb8-8a6f-55c54c18c7e2
Galkowski, Krzysztof
322994ac-7e24-4350-ab72-cc80ac8078ef
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Hladowski, Lukasz
db41c3fd-6c9e-48e8-81e7-9613072c59b5
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Nowicka, Weronika
e1ac7b8b-e806-4bb8-8a6f-55c54c18c7e2
Galkowski, Krzysztof
322994ac-7e24-4350-ab72-cc80ac8078ef
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72

Hladowski, Lukasz, Chen, Yiyang, Nowicka, Weronika, Galkowski, Krzysztof and Rogers, Eric (2016) Iterative learning control laws with full dynamics. In 2016 American Control Conference (ACC). IEEE. pp. 6369-6374 . (doi:10.1109/ACC.2016.7526671).

Record type: Conference or Workshop Item (Paper)

Abstract

Iterative learning control can be applied to systems that execute the same finite duration task over and over again. The distinguishing feature is the use of information from previous executions to construct the input to the next one in the sequence, including time domain information that would be non-causal in standard control systems. Many algorithms or laws have been developed for an ever increasing range of applications. This paper develops a new law which is fully dynamic, not static, when implemented. Experimental verification results are also given.

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Accepted/In Press date: 2016
e-pub ahead of print date: 6 July 2016
Published date: 1 August 2016
Venue - Dates: 2016 American Control Conference, ACC 2016, United States, 2016-07-05 - 2016-07-07
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 386885
URI: http://eprints.soton.ac.uk/id/eprint/386885
PURE UUID: 84fab120-3deb-4fa9-b085-2b3052e15428
ORCID for Yiyang Chen: ORCID iD orcid.org/0000-0001-9960-9040
ORCID for Weronika Nowicka: ORCID iD orcid.org/0000-0002-7049-1162
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398

Catalogue record

Date deposited: 03 Feb 2016 19:32
Last modified: 10 Sep 2020 01:44

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Contributors

Author: Lukasz Hladowski
Author: Yiyang Chen ORCID iD
Author: Weronika Nowicka ORCID iD
Author: Krzysztof Galkowski
Author: Eric Rogers ORCID iD

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