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

被引：18
作者：
Best M.C. [1 ]
Bogdanski K. [1 ]
机构：
[1] Department of Aeronautical and Automotive Engineering, Loughborough University, Ashby Road, Loughborough
来源：
Best, Matthew C. (m.c.best@lboro.ac.uk) | 1600年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 27期
基金：
英国工程与自然科学研究理事会;
关键词：
Kalman filter; Linear systems; Model order reduction; Nonlinear systems; System identification;
D O I：
10.1504/IJMIC.2017.082952
中图分类号：
学科分类号：
摘要：
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. © 2017 Inderscience Enterprises Ltd.
引用
收藏
页码：114 / 124
页数：10
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