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)

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 > Autonomous Systems and Connectivity
• 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.