Estimation of longitudinal speed robust to road conditions for ground vehicles
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Date
2016-06-14
Authors
Kasaiezadeh Mahabadi, Seyed Alireza
Khosravani, Saeid
Khajepour, Amir
Moshchuk, Nikolai
Chen, Shih-Ken
Hashemi, Ehsan
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis
Abstract
This article seeks to develop a longitudinal vehicle velocity estimator robust to road conditions by employing a tyre model at each corner. Combining the lumped LuGre tyre model and the vehicle kinematics, the tyres internal deflection state is used to gain an accurate estimation. Conventional kinematic-based velocity estimators use acceleration measurements, without correction with the tyre forces. However, this results in inaccurate velocity estimation because of sensor uncertainties which should be handled with another measurement such as tyre forces that depend on unknown road friction. The new Kalman-based observer in this paper addresses this issue by considering tyre nonlinearities with a minimum number of required tyre parameters and the road condition as uncertainty. Longitudinal forces obtained by the unscented Kalman filter on the wheel dynamics is employed as an observation for the Kalman-based velocity estimator at each corner. The stability of the proposed time-varying estimator is investigated and its performance is examined experimentally in several tests and on different road surface frictions. Road experiments and simulation results show the accuracy and robustness of the proposed approach in estimating longitudinal speed for ground vehicles.
Description
This is an Accepted Manuscript of an article published by Taylor & Francis in Vehicle System Dynamics on June 14 2016 available online: http://dx.doi.org/10.1080/00423114.2016.1178391
Keywords
Vehicle state estimation, Uncertain dynamics, Stability analysis, Parameter estimation, Unscented Kalman filter