Optimisation of the weighting functions of an H controller using genetic algorithms and structured genetic algorithms

Alfaro-Cid, E., McGookin, E.W. and Murray-Smith, D.J. (2008) Optimisation of the weighting functions of an H controller using genetic algorithms and structured genetic algorithms. International Journal of Systems Science, 39(4), pp. 335-347. (doi:10.1080/00207720701777959)

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Publisher's URL: http://dx.doi.org/10.1080/00207720701777959

Abstract

In this paper the optimisation of the weighting functions for an H controller using genetic algorithms and structured genetic algorithms is considered. The choice of the weighting functions is one of the key steps in the design of an H controller. The performance of the controller depends on these weighting functions since poorly chosen weighting functions will provide a poor controller. One approach that can solve this problem is the use of evolutionary techniques to tune the weighting parameters. The paper presents the improved performance of structured genetic algorithms over conventional genetic algorithms and how this technique can assist with the identification of appropriate weighting functions' orders.

Item Type:Articles
Additional Information:Due to publisher embargo the full text of this paper will not be available until April 2009.
Keywords:Optimisation, Genetic Algorithms, Hinf Control
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:McGookin, Dr Euan and Murray-Smith, Professor David
Authors: Alfaro-Cid, E., McGookin, E.W., and Murray-Smith, D.J.
Subjects:Q Science > QA Mathematics
College/School:College of Science and Engineering > School of Engineering > Aerospace Sciences
Journal Name:International Journal of Systems Science
Publisher:Taylor and Francis
ISSN:0020-7721
First Published:First published in International Journal of Systems Science 39(4):355-347
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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