Side slip angle estimation method and system based on robust unscented kalman filtering

Abstract

The invention discloses a side slip angle estimation method and system based on robust unscented kalman filtering. The method includes the steps that a three-freedom-degree vehicle dynamics model related to a side slip angle is established, a state equation and an observation equation of an unscented kalman filter are determined according to the model, and the input quantity, the state quantity and the observation quantity are determined, wherein the input quantity comprises the front wheel rotation angle and the longitudinal accelerated speed, the state quantity comprises the side slip angle,the yaw velocity and the longitudinal vehicle speed, and the observation quantity comprises the lateral accelerated speed and the yaw velocity; the weight factor is calculated through an M estimatoralgorithm; and in combination with the weight factor, the state quantity at the current moment, the state equation and the observation equation, the side slip angle at the next moment is estimated through an unscented kalman filtering algorithm. The M estimator algorithm and the unscented kalman filtering algorithm are combined to restrain the influence caused by outliers, the robustness of the unscented kalman filtering algorithm to the outliers in observation signals is improved, and the estimation accuracy of the vehicle side slip angle is improved.

Description

technical field [0001] The invention relates to the technical field of vehicle mass center sideslip angle estimation, in particular to a mass center side slip angle estimation method and system based on a robust unscented Kalman filter algorithm. Background technique [0002] The side slip angle of the vehicle center of mass is an important parameter to characterize the stability of the vehicle, so the side slip angle of the vehicle center of mass must be obtained when performing stability control. However, the vehicle center-of-mass side-slip angle sensor is expensive and is not suitable for mass-produced vehicles. Therefore, a parameter estimation method is required to estimate the vehicle center-of-mass side-slip angle. When building a vehicle model, the accuracy of the 2-degree-of-freedom linear model is poor, so a nonlinear model is often used to estimate the sideslip angle of the center of mass in order to obtain more accurate estimation results. [0003] The Kalman f...

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Problems solved by technology

[0004] However, the mechanism of the UKF method and the KF method are in the same strain, and they are both extensions of the recursive least squares method. Therefore, when the observed signal contains outliers, the estimation of the sideslip angle of the center of mass of the vehicle will be greatly affected, that is: the UKF method for Weak robustness to outliers in the observed signal

When estimating the side slip angle of the vehicle center of mass, the observation signal often comes from the vehicle sensor, and the sensor signal often has outliers, which seriously affects the accuracy of the UKF method for the estimation of the side slip angle of the center of mass.

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