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Comparison of Methods for Estimating Vehicle Side Slip Angle


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Comparison of Methods for Estimating Vehicle Side Slip Angle
LIN FenZHAO You-qun
Department of Automotive Engineering,Nanjing University of Aeronautics andAstronautics,Nanjing 210016,China
vehicles side slip angles radial basis functions neural network adaptive Kalman filter state estimation
Aiming at the problem that vehicle side slip angles are difficult to measure directly,a radial basis function(RBF) based neural network method is proposed to estimate side slip angles combined with driver-vehicle closed-loop system.Vehicle side slip angle is considered as mapping of time series of yaw rate and lateral acceleration.A uniform design project is used to select training samples,and the relationship of the three state parameters is established through neural network.An improved adaptive Kalman filter algorithm is designed to estimate vehicle side slip angles in the same road input.The two methods are compared based on full vehicle test: the average error and the standard deviation of RBF neural network method is 0.046 333°and 0.057 822° respectively.The average error and the standard deviation of Kalman filter method is 0.062 745°and 0.089 241° respectively.The conclusions can provide theoretic direction for design of estimator in vehicle stability control system.


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Last Update: 2012-11-19