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Download fileLinear MIMO model identification using an extended Kalman filter
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.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
XIII International Conference on Modelling, Identification and Control EngineeringVolume
1Issue
1Pages
1 - 6 (6)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.Publisher
© World Academy of Science Engineering and Technology (WASET)Version
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2015Notes
This paper was presented at: ICMICE 2015: 17th International Conference on Modelling, Identification and Control Engineering, 9th-10th July 2015, Prague, Czech Republic.Publisher version
Language
- en