LMI-based design of robust iterative learning control schemes with finite frequency range tracking specifications
LMI-based design of robust iterative learning control schemes with finite frequency range tracking specifications
Many systems compete the same finite duration task over and over again, where once each is complete the system resets to the starting location and the next one begins. Each execution is known as a trial and the duration the trial length. Iterative learning control has been developed for such systems where the distinguishing feature is the use of information from previous trials to update the control signal applied on the next one. The new contributions in this paper are for algorithms that use a feedforward filter often termed the learning filter. A condition for existence of this filter is formulated in terms of linear matrix inequalities through application of the generalized Kalman-Yakubovich-Popov lemma. This allows filter design over a finite, as opposed to the complete, frequency range which is more practically relevant in many cases. An extension to systems with uncertainties represented by a polytopic description is also developed using parameter dependent Lyapunov functions.
Paszke, W.
dd4b8f12-17c7-45ee-bfb0-e9675bd7d854
Rogers, E.
611b1de0-c505-472e-a03f-c5294c63bb72
Galkowski, K.
40c02cf5-8fcb-44de-bb1e-f9f70fdd265d
19 June 2013
Paszke, W.
dd4b8f12-17c7-45ee-bfb0-e9675bd7d854
Rogers, E.
611b1de0-c505-472e-a03f-c5294c63bb72
Galkowski, K.
40c02cf5-8fcb-44de-bb1e-f9f70fdd265d
Paszke, W., Rogers, E. and Galkowski, K.
(2013)
LMI-based design of robust iterative learning control schemes with finite frequency range tracking specifications.
2013 American Control Conference, , Washington DC, United States.
16 - 18 Jun 2013.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Many systems compete the same finite duration task over and over again, where once each is complete the system resets to the starting location and the next one begins. Each execution is known as a trial and the duration the trial length. Iterative learning control has been developed for such systems where the distinguishing feature is the use of information from previous trials to update the control signal applied on the next one. The new contributions in this paper are for algorithms that use a feedforward filter often termed the learning filter. A condition for existence of this filter is formulated in terms of linear matrix inequalities through application of the generalized Kalman-Yakubovich-Popov lemma. This allows filter design over a finite, as opposed to the complete, frequency range which is more practically relevant in many cases. An extension to systems with uncertainties represented by a polytopic description is also developed using parameter dependent Lyapunov functions.
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Published date: 19 June 2013
Venue - Dates:
2013 American Control Conference, , Washington DC, United States, 2013-06-16 - 2013-06-18
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 352232
URI: http://eprints.soton.ac.uk/id/eprint/352232
PURE UUID: eaa75384-4b85-4281-b4d8-0f38edfd9c72
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Date deposited: 08 May 2013 14:15
Last modified: 09 Jan 2022 02:40
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Contributors
Author:
W. Paszke
Author:
E. Rogers
Author:
K. Galkowski
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