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
Hladowski, Lukasz
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Chen, Yiyang
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Nowicka, Weronika
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Galkowski, Krzysztof
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Rogers, Eric
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1 August 2016
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.
.
(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, , Boston, United States, 2016-07-06 - 2016-07-08
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
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Date deposited: 03 Feb 2016 19:32
Last modified: 16 Mar 2024 02:41
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Contributors
Author:
Lukasz Hladowski
Author:
Yiyang Chen
Author:
Weronika Nowicka
Author:
Krzysztof Galkowski
Author:
Eric Rogers
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