Longitudinal vehicle state estimation using nonlinear and parameter-varying observers
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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.
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Ehsan Hashemi, Saeid Khosravani, Amir Khajepour, Seyed Alireza Kasaiezadeh Mahabadi, Shih-Ken Chen, Baktiar Litkouhi (2017). Longitudinal vehicle state estimation using nonlinear and parameter-varying observers. UWSpace. http://hdl.handle.net/10012/11881
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