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Mathematical Problems in Engineering
Volume 2016, Article ID 1715762, 15 pages
Research Article

Application of Recursive Subspace Method in Vehicle Lateral Dynamics Model Identification

1Beijing Institute of Mechanical Equipment, Beijing 100854, China
2School of Mechanical Engineering, State Key Laboratory for Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China

Received 14 September 2015; Revised 9 January 2016; Accepted 20 March 2016

Academic Editor: Laurent Bako

Copyright © 2016 Tengyue Ba et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Modeling of vehicle behavior based on the identification method has received a renewed attention in recent years. In order to improve the linear time-invariant vehicle identification model, a more general identifiable vehicle model structure is proposed, in which time-varying characteristics of vehicle speed and cornering stiffness are taken into consideration. To identify the proposed linear time-varying vehicle model, a well-established data-driven method, named recursive optimized version of predictor-based subspace identification, is introduced. Before vehicle model identification, the influences of four parameters in the subspace algorithm are studied based on pulse input road test. And then a set of practical optimal parameters are chosen and used for the vehicle model identification. Through a series of standard road tests under different maneuvers, the linear time-varying vehicle model can be identified in real-time. It is demonstrated by comparison that the predicted outputs of the proposed vehicle model are much closer to the real vehicle outputs and the model has a wider range of application.