scholarly journals A model predictive approach to control vehicle skidding by sideslip angle estimation

2021 ◽  
pp. 041-046
Author(s):  
Graffione Simone ◽  
Sacile Roberto ◽  
Zero Enrico ◽  
Bersani Chiara
Keyword(s):  
Sideslip Angle ◽  
Angle Estimation ◽  
Predictive Approach

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

Virtual sensor for real-time estimation of the vehicle sideslip angle

Sensor Review ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 255-272
Author(s):  
Kanwar Bharat Singh
Keyword(s):  
Steady State ◽  
Unscented Kalman Filter ◽  
Time Estimation ◽  
Experimental Tests ◽  
Slip Angle ◽  
Successful Implementation ◽  
Tire Model ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Content Type

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.

scholarly journals On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018 ◽  
Vol 8 (3) ◽  
pp. 355 ◽  
Author(s):  
Daniel Chindamo ◽  
Basilio Lenzo ◽  
Marco Gadola
Keyword(s):  
Literature Review ◽  
Sideslip Angle ◽  
Angle Estimation

Vehicle sideslip angle estimation via a Riccati equation based nonlinear filter

Meccanica ◽  
2017 ◽  
Vol 52 (15) ◽  
pp. 3513-3529 ◽  
Author(s):  
Salvatore Strano ◽  
Mario Terzo
Keyword(s):  
Riccati Equation ◽  
Nonlinear Filter ◽  
Sideslip Angle ◽  
Angle Estimation
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