Analysis of Linear Iterative Learning Control Schemes - A 2D Systems /Repetitive Processes Approach
Analysis of Linear Iterative Learning Control Schemes - A 2D Systems /Repetitive Processes Approach
This paper first develops results on the stability and convergence properties of a general class of linear iterative learning control schemes using, in the main, theory first developed for the class of 2D linear systems known as linear repetitive processes. A general learning law that uses information from the current and a finite number of previous trials is considered and the results, in the form of fundamental limitations on the benefits of using this law, are interpreted in terms of basic systems theoretic concepts such as the relative degree and minimum phase characteristics of the example under consideration. Following this, previously reported powerful 2d predictive and adaptive control algorithms are reviewed. Finally, new iterative adaptive learning control laws which solve iterative learning control problems under very weak assumptions are developed.
125-77
Owens, D H
db24b8ef-282b-47c0-9cd2-75e91d312ad7
Amann, N
4ae6bc24-2ac6-438b-8408-08f3dfeef9bc
Rogers, E
611b1de0-c505-472e-a03f-c5294c63bb72
French, M
22958f0e-d779-4999-adf6-2711e2d910f8
2000
Owens, D H
db24b8ef-282b-47c0-9cd2-75e91d312ad7
Amann, N
4ae6bc24-2ac6-438b-8408-08f3dfeef9bc
Rogers, E
611b1de0-c505-472e-a03f-c5294c63bb72
French, M
22958f0e-d779-4999-adf6-2711e2d910f8
Owens, D H, Amann, N, Rogers, E and French, M
(2000)
Analysis of Linear Iterative Learning Control Schemes - A 2D Systems /Repetitive Processes Approach.
Multidimensional Systems and Signal Processing, 11 (1/2), .
Abstract
This paper first develops results on the stability and convergence properties of a general class of linear iterative learning control schemes using, in the main, theory first developed for the class of 2D linear systems known as linear repetitive processes. A general learning law that uses information from the current and a finite number of previous trials is considered and the results, in the form of fundamental limitations on the benefits of using this law, are interpreted in terms of basic systems theoretic concepts such as the relative degree and minimum phase characteristics of the example under consideration. Following this, previously reported powerful 2d predictive and adaptive control algorithms are reviewed. Finally, new iterative adaptive learning control laws which solve iterative learning control problems under very weak assumptions are developed.
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Published date: 2000
Organisations:
Southampton Wireless Group
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Local EPrints ID: 252225
URI: http://eprints.soton.ac.uk/id/eprint/252225
PURE UUID: 1d6f5cf0-f167-4945-a744-6dd83e156f75
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Date deposited: 05 Mar 2004
Last modified: 18 Oct 2022 01:32
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Author:
D H Owens
Author:
N Amann
Author:
E Rogers
Author:
M French
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