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.
|Number of pages||7|
|Journal||Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering|
|Publication status||Published - 1 May 2000|
- system identification
- model reduction
- robust control
- evolutionary algorithms