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Tire-road friction coefficient information is of critical importance for vehicle dynamic control such as yaw stability control, trajectory tracking control, and rollover prevention for both manned and unmanned applications. Existing tire-road friction coefficient estimation approaches often require certain levels of vehicle longitudinal and/or lateral motion excitations (e.g. accelerating, decelerating,...
For a fully automation ground vehicle, the accurate longitudinal speed becomes more and more important due to the increasing demands on tracking of position, velocity and acceleration. Based on low-cost wheel speed encoders, this paper proposes a novel longitudinal speed estimator for vehicles on cornering maneuver. In the estimator, a gain-varying Kalman estimator is designed to output lateral states...
This paper presents a method of estimating vehicle states using an Unscented Kalman filter (UKF). The UKF developed estimates Vehicle motion, such as yaw rate and side slip angle, from the noisy measurement set. The vehicle state estimation using a non-linear vehicle model with Unitire tire model will be compared to the measured state which is subjected to the same tests, in order to validate the...
The unscented Kalman filter (UKF) has become a new technique used in a number of nonlinear estimation application. Its application process includes calculating and transmitting the mean and covariance; making use of the forecast sample points and weighing calculation to forecast the mean and covariance; forecasting measurement value and covariance; at last, calculating the UKF gain, renewing state...
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