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Linear MIMO model identification using an extended Kalman filter

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conference contribution
posted on 29.04.2015, 13:13 by Matt Best
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 Engineering

Volume

1

Issue

1

Pages

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

2015

Notes

This paper was presented at: ICMICE 2015: 17th International Conference on Modelling, Identification and Control Engineering, 9th-10th July 2015, Prague, Czech Republic.

Language

en

Location

Prague, Czech Republic

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