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Multivariable norm optimal iterative learning control with auxiliary optimization

Multivariable norm optimal iterative learning control with auxiliary optimization
Multivariable norm optimal iterative learning control with auxiliary optimization
The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously converging to the solution of a constrained quadratic optimization problem. The theory is presented in a general functional analytical framework using operators between chosen real Hilbert spaces. This is applied to solve problems in continuous time where tracking is only required at selected intermediate points of the time interval but, simultaneously, the solution is required to minimize a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. Applications to the discrete time case, including the case of multi-rate sampling, are also summarized. The algorithms are motivated by practical need and provide a methodology for reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear whilst maintaining the desired tracking accuracy necessary for task completion. Solutions in terms of NOILC methodologies involving both feedforward and feedback components offer the possibilities of greater robustness than purely feedforward actions. Robustness of the feedforward implementation is discussed and the work is illustrated by experimental results from a robotic manipulator.
0020-3270
1026-1045
Owens, D.H.
3452e9bb-d3bd-4995-b4bb-424bbd288b09
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Owens, D.H.
3452e9bb-d3bd-4995-b4bb-424bbd288b09
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f

Owens, D.H., Freeman, C.T. and Chu, B. (2013) Multivariable norm optimal iterative learning control with auxiliary optimization. International Journal of Control, 86 (6), 1026-1045.

Record type: Article

Abstract

The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously converging to the solution of a constrained quadratic optimization problem. The theory is presented in a general functional analytical framework using operators between chosen real Hilbert spaces. This is applied to solve problems in continuous time where tracking is only required at selected intermediate points of the time interval but, simultaneously, the solution is required to minimize a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. Applications to the discrete time case, including the case of multi-rate sampling, are also summarized. The algorithms are motivated by practical need and provide a methodology for reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear whilst maintaining the desired tracking accuracy necessary for task completion. Solutions in terms of NOILC methodologies involving both feedforward and feedback components offer the possibilities of greater robustness than purely feedforward actions. Robustness of the feedforward implementation is discussed and the work is illustrated by experimental results from a robotic manipulator.

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Published date: 13 April 2013
Organisations: EEE, Southampton Wireless Group

Identifiers

Local EPrints ID: 340174
URI: http://eprints.soton.ac.uk/id/eprint/340174
ISSN: 0020-3270
PURE UUID: 5645231b-15f6-47b7-a8f9-17e63e49c30f
ORCID for B. Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 13 Jun 2012 22:34
Last modified: 03 Oct 2020 01:39

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