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：

International Journal of Modelling, Identification and Control | 2017年 / 27卷 / 02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

页码：114 / 124

页数：10

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