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.
Original language | English |
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Pages (from-to) | 1125-1130 |
Number of pages | 6 |
Journal | IEE Conference Publication |
Issue number | 427 /2 |
Publication status | Published - 1 Dec 1996 |
Externally published | Yes |
Keywords
- genetic algorithms
- system identification
- model reduction
- robust control applications