Longitudinal vehicle state estimation using nonlinear and parameter-varying observers
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Date
2017-05
Authors
Hashemi, Ehsan
Khosravani, Saeid
Khajepour, Amir
Kasaiezadeh Mahabadi, Seyed Alireza
Chen, Shih-Ken
Litkouhi, Baktiar
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
A corner-based velocity estimation approach is proposed which is used for vehicle’s traction and stability control systems. This approach incorporates internal tire states within the vehicle kinematics and enables the velocity estimator to work for a wide range of maneuvers without road friction information. Tire models have not been widely implemented in velocity estimators because of uncertain road friction and varying tire parameters, but the current study utilizes a simplified LuGre model with the minimum number of tire parameters and estimates velocity robust to model uncertainties. The proposed observer uses longitudinal forces, updates the states by minimizing the longitudinal force estimation error, and provides accurate outcomes at each tire. The estimator structure is shown to be robust to road conditions and rejects disturbances and model uncertainties effectively. Taking into account the vehicle dynamics is time-varying, the stability of the observer for the linear parameter varying model is proved, time-varying observer gains are designed, and the performance is studied. Road test experiments have been conducted and the results are used to validate the proposed approach.
Description
The final publication is available at Elsevier via https://doi.org/10.1016/j.mechatronics.2017.02.004 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
Vehicle state estimation, Velocity estimation, Parametervarying systems, Uncertain dynamics, Unscented Kalman filter