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

Authors:
E. Alfaro-Cid;E. W. McGookin;D. J. Murray-Smith
Affiliations:
Instituto Tecnológico de Informática, Valencia, Spain;Dept. of Aerospace Engineering, University of Glasgow, UK;Dept. of Electronics and Electrical Engineering, University of Glasgow, UK
Venue:
International Journal of Systems Science
Year:
2008

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Abstract

In this article, 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 article 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.