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.
Original language | English |
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Pages (from-to) | 231-237 |
Number of pages | 7 |
Journal | Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering |
Volume | 214 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 May 2000 |
Keywords
- system identification
- model reduction
- robust control
- evolutionary algorithms