Article: Extending the Kalman filter for structured identification of linear and nonlinear systems Journal: International Journal of Modelling, Identification and Control (IJMIC) 2017 Vol.27 No.2 pp.114 - 124 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Matthew C. Best; Karol Bogdanski

Addresses: Department of Aeronautical and Automotive Engineering, Loughborough University, Ashby Road, Loughborough, LE11 3TU, UK ' Department of Aeronautical and Automotive Engineering, Loughborough University, Ashby Road, Loughborough, LE11 3TU, UK

Abstract: 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.

Keywords: system identification; model order reduction; extended Kalman filter; EKF; linear systems; nonlinear systems; vehicle modelling; handling response; vehicle handling; full vehicle models.

DOI: 10.1504/IJMIC.2017.082952

International Journal of Modelling, Identification and Control, 2017 Vol.27 No.2, pp.114 - 124

Received: 31 Oct 2015
Accepted: 14 Mar 2016
Published online: 16 Mar 2017 *

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

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