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Singular value distribution of non-minimum phase systems with application to iterative learning control

Singular value distribution of non-minimum phase systems with application to iterative learning control
Singular value distribution of non-minimum phase systems with application to iterative learning control
This paper provides a rigorous mathematical analysis on the singular value distributions of input-output matrices for discrete time non-minimum phase (NMP) systems. It is shown that when the time scale considered is sufficiently long, the input-output matrix of a NMP system has m infinitesimally small singular values, the rest of which are significantly large with a non-zero lower bound, where m is the number of NMP zeros in the NMP systems. It is the existence of these m nearly zero singular values that causes various difficulties in analysis and design for NMP systems. The corresponding singular vector spaces can also be characterised. The analysis results are further applied to a gradient-based iterative learning control algorithm to analyse a well-known problematic slow convergence phenomenon and numerical simulations are presented to verify the theoretical predictions
6700-6705
IEEE
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Owens, David
52e6b7af-bacf-4067-ab53-6800d7e19825
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Owens, David
52e6b7af-bacf-4067-ab53-6800d7e19825

Chu, B. and Owens, David (2013) Singular value distribution of non-minimum phase systems with application to iterative learning control. In Proceedings of the IEEE Conference on Decision and Control. IEEE. pp. 6700-6705 . (doi:10.1109/CDC.2013.6760950).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper provides a rigorous mathematical analysis on the singular value distributions of input-output matrices for discrete time non-minimum phase (NMP) systems. It is shown that when the time scale considered is sufficiently long, the input-output matrix of a NMP system has m infinitesimally small singular values, the rest of which are significantly large with a non-zero lower bound, where m is the number of NMP zeros in the NMP systems. It is the existence of these m nearly zero singular values that causes various difficulties in analysis and design for NMP systems. The corresponding singular vector spaces can also be characterised. The analysis results are further applied to a gradient-based iterative learning control algorithm to analyse a well-known problematic slow convergence phenomenon and numerical simulations are presented to verify the theoretical predictions

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More information

Published date: 13 December 2013
Venue - Dates: 52nd IEEE Conference on Decision and Control, , Florence, Italy, 2013-12-10 - 2013-12-13

Identifiers

Local EPrints ID: 472514
URI: http://eprints.soton.ac.uk/id/eprint/472514
PURE UUID: db338257-9ea3-42cf-9d57-637be2a4ded2
ORCID for B. Chu: ORCID iD orcid.org/0000-0002-2711-8717

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Date deposited: 07 Dec 2022 17:45
Last modified: 17 Mar 2024 03:28

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Contributors

Author: B. Chu ORCID iD
Author: David Owens

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