2134/17386
Matt Best
Matt
Best
Linear MIMO model identification using an extended Kalman filter
Loughborough University
2015
System identification
Kalman filter
Linear model
MIMO
Model order reduction
Engineering not elsewhere classified
2015-04-29 13:13:44
Conference contribution
https://repository.lboro.ac.uk/articles/conference_contribution/Linear_MIMO_model_identification_using_an_extended_Kalman_filter/9223106
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.