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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/17386

Title: Linear MIMO model identification using an extended Kalman filter
Authors: Best, Matt C.
Keywords: System identification
Kalman filter
Linear model
Model order reduction
Issue Date: 2015
Publisher: © World Academy of Science Engineering and Technology (WASET)
Citation: BEST, M.C., 2015. Linear MIMO model identification using an extended Kalman filter. World Academy of Science, Engineering and Technology Mechanical and Mechatronics Engineering, 2 (7), 6pp.
Abstract: Linear Multi-Input Multi-Output (MIMO) dynamic models can be identified, with no a priori knowledge of model structure or order, using a new Generalised Identifying Filter (GIF). Based on an Extended Kalman Filter, the new filter identifies the model iteratively, in a continuous modal canonical form, using only input and output time histories. The filter’s self-propagating state error covariance matrix allows easy determination of convergence and conditioning, and by progressively increasing model order, the best fitting reduced-order model can be identified. The method is shown to be resistant to noise and can easily be extended to identification of smoothly nonlinear systems.
Description: This paper was presented at: ICMICE 2015: 17th International Conference on Modelling, Identification and Control Engineering, 9th-10th July 2015, Prague, Czech Republic.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/17386
Publisher Link: https://waset.org/abstracts/24532
Appears in Collections:Conference Papers and Contributions (Aeronautical and Automotive Engineering)

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