Numerical Achieved Extended Kalman Filter State Observer Design Based on a Vehicle Model Containing UniTire Model

2008 ◽  
Author(s):  
Pan Zhao ◽  
Changfu Zong ◽  
Dan Hu ◽  
Hongyu Zheng ◽  
Kan Wu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
State Observer ◽  
Observer Design ◽  
Vehicle Model

Application of the Extended Kalman Filter to a Planar Vehicle Model to Predict the Onset of Jackknife Instability

2004 ◽  
Author(s):  
Ashley L. (Al) Dunn ◽  
Gary J. Heydinger ◽  
Giorgio Rizzoni ◽  
Dennis A. Guenther
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Vehicle Model

Verification of nonlinear state observer system for RazakSAT using extended Kalman filter

2021 ◽  
Author(s):  
H. Nor Hazadura ◽  
J. Ahmad Kadri ◽  
H. Mohd Zamri ◽  
T. Vilcherd
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
State Observer ◽  
Observer System ◽  
Nonlinear State

Nonlinear estimator design based on extended Kalman filter approach for state estimation of articulated heavy vehicle

2018 ◽  
Vol 233 (2) ◽  
pp. 254-265 ◽  
Author(s):  
Hamze Ahmadi Jeyed ◽  
Ali Ghaffari
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
High Velocity ◽  
The State ◽  
Heavy Vehicle ◽  
Lane Change ◽  
Vehicle Model ◽  
Articulated Vehicle ◽  
Simulation Results

In this article, in order to measure the state variables directly in an articulated heavy vehicle, the extended Kalman filter approaches are proposed. For this purpose, using Kane’s method, a nonlinear model is developed for the articulated vehicle, including the motion equations of longitudinal, lateral, and yaw motion of the tractor, and the hitch articulation angle between the tractor and the semi-trailer. Using TruckSim software, the articulated vehicle model is verified through high-velocity lane change maneuver (a single sinusoidal wave with an amplitude of 5° and a frequency of 0.5 Hz) under the dry and slippery road condition. The simulation results showed that the proposed model is close to the real vehicle model and can be used in the estimator development. Then, the state estimation algorithm is designed and implemented using extended Kalman filter for real-time estimation of the states. To evaluate the performance of the extended Kalman filter, simulations with two maneuvers including high-velocity lane change maneuvers in the dry road and slippery road are carried out. The simulation results demonstrate the impressive performance of the extended Kalman filter for state estimation of the articulated vehicle in critical conditions such as the slippery road and the high velocity.

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