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|Title:||Evolutionary L ∞ identification and model reduction for robust control||Authors:||Tan, K.C.
|Issue Date:||2000||Citation:||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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