Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/80405
Title: Evolutionary L ∞ identification and model reduction for robust control
Authors: Tan, K.C. 
Li, Y.
Issue Date: 2000
Source: Tan, K.C.,Li, Y. (2000). Evolutionary L ∞ identification and model reduction for robust control. Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering 214 (3) : 231-237. ScholarBank@NUS Repository.
Abstract: An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a `worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L ∞ error bound than existing methods in the literature do.
Source Title: Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/80405
ISSN: 09596518
Appears in Collections:Staff Publications

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