Knowledge of vehicle dynamic parameters is important for vehicle control systems that aim to enhance vehicle handling and passenger safety. Unfortunately, some fundamental parameters like tire-road forces and sideslip angle are difficult to measure in a car, for both technical and economic reasons. Therefore, this study presents a dynamic modeling and observation method to estimate these variables. The ability to accurately estimate lateral tire forces and sideslip angle is a critical determinant in the performances of many vehicle control system and making vehicle's danger indices. To address system nonlinearities and unmodeled dynamics, an observer derived from unscented Kalman filtering technique is proposed. The estimation process method is based on the dynamic response of a vehicle instrumented with easily-available standard sensors. Performances are tested using an experimental car in real driving situations. Experimental results show the potentiel of the estimation method.
Unscented Kalman filter for real-time vehicle lateral tire forces and sideslip angle estimation
2009 IEEE Intelligent Vehicles Symposium ; 901-906
2009-06-01
1352104 byte
Conference paper
Electronic Resource
English
Unscented Kalman Filter for Real-time Vehicle Lateral Tire Forces and Sideslip Angle Estimation
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