Application of Recursive Subspace Method in Vehicle Lateral Dynamics Model Identification

Abstract EN

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.