Kay, C.T. and Li, Y. (1996) L∞ identification and model reduction using a learning genetic algorithm. In: Control '96, UKACC International Conference, Exeter, UK, Sep 1996, pp. 1125-1130. (doi: 10.1049/cp:19960711)
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Publisher's URL: http://dx.doi.org/10.1049/cp:19960711
Abstract
This paper develops a Boltzmann learning enhanced genetic algorithm for L∞ norm based system identification and model reduction for robust control applications. Using this technique, both a globally optimised nominal model and an error bounding function for additive and multiplicative uncertainties can be obtained. It can also offer a tighter L∞ error bound and is applicable to both continuous and discrete-time systems.
| Item Type: | Conference Proceedings |
|---|---|
| Additional Information: | ISBN: 0852966687 |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Li, Professor Yun |
| Authors: | Kay, C.T., and Li, Y. |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| College/School: | College of Science and Engineering > School of Engineering > Systems Power and Energy |
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