Iterative learning control for a non-minimum phase plant based on a reference shift algorithm
Iterative learning control for a non-minimum phase plant based on a reference shift algorithm
In order to improve the tracking performance of a non-minimum phase plant, a new method called the reference shift algorithm has been developed to overcome the problem of output lag encountered when using traditional feedback control combined with basic forms of iterative learning control. In the proposed algorithm a hybrid approach has been adopted in order to generate the next input signal. One learning loop addresses the system lag and another tackles the possibility of a large initial plant input commonly encountered when using basic iterative learning control algorithms. Simulations and experimental results have shown that there is a significant improvement in tracking performance when using this approach compared with that of other iterative learning control algorithms that have been implemented on the non-minimum phase experimental test facility
633-643
Cai, Z
dd8dd525-19a5-4792-a048-617340996afe
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rogers, E.
611b1de0-c505-472e-a03f-c5294c63bb72
2008
Cai, Z
dd8dd525-19a5-4792-a048-617340996afe
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rogers, E.
611b1de0-c505-472e-a03f-c5294c63bb72
Cai, Z, Freeman, C.T., Lewin, P.L. and Rogers, E.
(2008)
Iterative learning control for a non-minimum phase plant based on a reference shift algorithm.
Control Engineering Practice, 16 (6), .
(doi:10.1016/j.conengprac.2007.07.001).
Abstract
In order to improve the tracking performance of a non-minimum phase plant, a new method called the reference shift algorithm has been developed to overcome the problem of output lag encountered when using traditional feedback control combined with basic forms of iterative learning control. In the proposed algorithm a hybrid approach has been adopted in order to generate the next input signal. One learning loop addresses the system lag and another tackles the possibility of a large initial plant input commonly encountered when using basic iterative learning control algorithms. Simulations and experimental results have shown that there is a significant improvement in tracking performance when using this approach compared with that of other iterative learning control algorithms that have been implemented on the non-minimum phase experimental test facility
More information
Published date: 2008
Organisations:
EEE, Southampton Wireless Group, Aerodynamics & Flight Mechanics Group
Identifiers
Local EPrints ID: 264364
URI: http://eprints.soton.ac.uk/id/eprint/264364
ISSN: 0967-0661
PURE UUID: 8bb8839c-3cf1-4d15-81d0-ac6ed0c6dec6
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Date deposited: 02 Aug 2007
Last modified: 11 Dec 2024 02:39
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