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Extending the Kalman filter for structured identification of linear and nonlinear systems

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journal contribution
posted on 24.03.2016, 11:02 by Matt Best, Karol Bogdanski
This paper considers a novel approach to system identification which allows accurate models to be created for both linear and nonlinear multi-input / output systems. In addition to conventional system identification applications the method can also be used as a black-box tool for model order reduction. A nonlinear Kalman filter is extended to include slow-varying parameter states in a canonical model structure. Interestingly, in spite of all model parameters being unknown at the start, the filter is able to evolve parameter estimates to achieve 100% accuracy in noise-free test cases, and is also proven to be robust to noise in the measurements. The canonical structure ensures a well-conditioned model which simultaneously provides valuable dynamic information to the engineer. After extensive testing of a linear example, the model structure is extended to a generalised nonlinear form, which is shown to accurately identify the handling response of a full vehicle model.

Funding

This work was supported by Jaguar Land Rover and the UK-EPSRC grant EP/K014102/1 as part of the jointly funded Programme for Simulation Innovation (PSi).

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

International Journal of Modelling, Identification and Control

Pages

1 - 1 (19)

Citation

BEST, M.C. and BOGDANSKI, K., 2016. Extending the Kalman filter for structured identification of linear and nonlinear systems. International Journal of Modelling, Identification and Control, 27 (2), pp. 114-124.

Publisher

Inderscience © The Author(s)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 (CC BY 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Publication date

2016

Notes

This is an Open Access article published by Inderscience and distributed under the terms of the Creative Commons Attribution Licence, https://creativecommons.org/licenses/by/4.0/

ISSN

1746-6172

eISSN

1746-6180

Language

en

Licence

Exports