Wheel Slip Angle Estimation of a Planar Mobile Platform

Author(s):  
Ronnapee Chaichaowarat ◽  
Witaya Wannasuphoprasit
2016 ◽  
Vol 20 (2) ◽  
pp. 89-107 ◽  
Author(s):  
Ronnapee Chaichaowarat ◽  
Witaya Wannasuphoprasit

2011 ◽  
Vol 56 (1/2/3/4) ◽  
pp. 161 ◽  
Author(s):  
ang Li ◽  
Jian Song ◽  
Hongzhi Li ◽  
Zhang Xiaolong ◽  
ang Li ◽  
...  

2011 ◽  
Vol 56 (5) ◽  
pp. 1163-1170 ◽  
Author(s):  
Gridsada Phanomchoeng ◽  
Rajesh Rajamani ◽  
Damrongrit Piyabongkarn

Sensor Review ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 255-272
Author(s):  
Kanwar Bharat Singh

Purpose The vehicle sideslip angle is an important state of vehicle lateral dynamics and its knowledge is crucial for the successful implementation of advanced driver-assistance systems. Measuring the vehicle sideslip angle on a production vehicle is challenging because of the exorbitant price of a physical sensor. This paper aims to present a novel framework for virtually sensing/estimating the vehicle sideslip angle. The desired level of accuracy for the estimator is to be within +/− 0.2 degree of the actual sideslip angle of the vehicle. This will make the precision of the proposed estimator at par with expensive commercially available sensors used for physically measuring the vehicle sideslip angle. Design/methodology/approach The proposed estimator uses an adaptive tire model in conjunction with a model-based observer. The performance of the estimator is evaluated through experimental tests on a rear-wheel drive vehicle. Findings Detailed experimental results show that the developed system can reliably estimate the vehicle sideslip angle during both steady state and transient maneuvers, within the desired accuracy levels. Originality/value This paper presents a novel framework for vehicle sideslip angle estimation. The presented framework combines an adaptive tire model, an unscented Kalman filter-based axle force observer and data from tire mounted sensors. Tire model adaptation is achieved by making extensions to the magic formula, by accounting for variations in the tire inflation pressure, load, tread-depth and temperature. Predictions with the adapted tire model were validated by running experiments on the Flat-Trac® machine. The benefits of using an adaptive tire model for sideslip angle estimation are demonstrated through experimental tests. The performance of the observer is satisfactory, in both transient and steady state maneuvers. Future work will focus on measuring tire slip angle and road friction information using tire mounted sensors and using that information to further enhance the robustness of the vehicle sideslip angle observer.


2016 ◽  
Author(s):  
Herman M. Kaharmen ◽  
Djoko Kustono ◽  
Waras Kamdi ◽  
Tuwoso ◽  
Poppy Puspitasari

Robotica ◽  
2008 ◽  
Vol 27 (6) ◽  
pp. 801-811 ◽  
Author(s):  
Z. B. Song ◽  
L. D. Seneviratne ◽  
K. Althoefer ◽  
X. J. Song ◽  
Y. H. Zweiri

SUMMARYSliding mode observer is a variable structure system where the dynamics of a nonlinear system is altered via application of a high-frequency switching control. This paper presents a non-linear sliding mode observer for wheel linear slip and slip angle estimation of a single wheel based on its kinematic model and velocity measurements with added noise to simulate actual on-board sensor measurements. Lyapunov stability theory is used to establish the stability conditions for the observer. It is shown that the observer will converge in a finite time, provided the observer gains satisfy constraints based on a stability analysis. To validate the observer, linear and two-dimensional (2D) test rigs are specially designed. The sliding mode observer is tested under a variety of conditions and it is shown that the sliding mode observer can estimate wheel slip and slip angle to a high accuracy. It is also shown that the sliding mode observer can accurately predict wheel slip and slip angle in the presence of noise, by testing the performance of the sliding mode observer after adding white noise to the measurements. An extended Kalman filter is also developed for comparison purposes. The sliding mode observer is better in terms of prediction accuracy.


2013 ◽  
Vol 46 (10) ◽  
pp. 286-291 ◽  
Author(s):  
Seung-Hi Lee ◽  
Youngseop Son ◽  
Chang Mook Kang ◽  
Chung Choo Chung

2014 ◽  
Vol 22 (5) ◽  
pp. 2048-2055 ◽  
Author(s):  
Massimo Canale ◽  
Lorenzo Fagiano ◽  
Carlo Novara

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