Extending the Kalman filter for structured identification of linear and nonlinear systems
journal contributionposted on 24.03.2016 by Matt Best, Karol Bogdanski
Any type of content formally published in an academic journal, usually following a peer-review process.
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.
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).
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering