scholarly journals Vehicle Sideslip Angle Estimation for a Heavy-Duty Vehicle via Extended Kalman Filter Using a Rational Tyre Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 142120-142130
Author(s):  
Feliciano Di Biase ◽  
Basilio Lenzo ◽  
Francesco Timpone
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Heavy Duty ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Tyre Model ◽  
Heavy Duty Vehicle

Sideslip angle estimation using extended Kalman filter

2008 ◽  
Vol 46 (sup1) ◽  
pp. 353-364 ◽  
Author(s):  
B.-C. Chen ◽  
F.-C. Hsieh
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sideslip Angle ◽  
Angle Estimation

A variable structure adaptive extended Kalman filter for vehicle slip angle estimation

2011 ◽  
Vol 56 (1/2/3/4) ◽  
pp. 161 ◽  
Author(s):  
ang Li ◽  
Jian Song ◽  
Hongzhi Li ◽  
Zhang Xiaolong ◽  
ang Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Variable Structure ◽  
Slip Angle ◽  
Angle Estimation ◽  
Adaptive Extended Kalman Filter

Vehicle sideslip angle estimation using deep ensemble-based adaptive Kalman filter

2020 ◽  
Vol 144 ◽  
pp. 106862 ◽  
Author(s):  
Dongchan Kim ◽  
Kyushik Min ◽  
Hayoung Kim ◽  
Kunsoo Huh
Keyword(s):  
Kalman Filter ◽  
Adaptive Kalman Filter ◽  
Sideslip Angle ◽  
Angle Estimation

Comparison of Nonlinear Receding-Horizon and Extended Kalman Filter Strategies for Ground Vehicles Longitudinal Slip Estimation

2020 ◽  
Author(s):  
Elias Dias Rossi Lopes ◽  
Gustavo Simão Rodrigues ◽  
Helon Vicente Hultmann Ayala
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Autonomous Vehicles ◽  
Contact Forces ◽  
Control Synthesis ◽  
Sideslip Angle ◽  
Estimation Algorithms ◽  
Longitudinal Slip ◽  
Tire Forces

Friction efforts are present in almost all mechanical applications, due to contact between bodies and there are many important situations, in which they must be properly controlled. Among these, there are tire contact forces, which is focus of many studies in autonomous vehicles and control applications on vehicle systems, since the tire forces and moments are nonlinear and may be modelled as friction efforts. Any control synthesis focused to optimize its performance must be associated to state estimators, since the efforts depend on slip variables, as longitudinal slip and sideslip angle, and it is not possible to accurately measure them. So, in this paper, two state estimation algorithms are evaluated: Extended Kalman Filter (EKF) and Moving Horizon State Estimation (MHSE), which are applied to a quarter-car model for longitudinal dynamics. It is presented that, for both traction and braking phases, the MHSE is more accurate, since it takes explicitly into account the nonlinear model in the estimation process, independently of Jacobian sensitivities to discontinuities as is the case here. So, it is demonstrated that the developed estimator may be successfully associated to controllers with the objective of optimize tire performance in traction and braking control.

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