Linear MIMO model identification using an extended Kalman filter
conference contributionposted on 29.04.2015 by Matt Best
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
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.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering