基于自适应模糊扩展卡尔曼滤波的车辆运动状态联合估计

Joint Estimation of Vehicle Motion State Based on Adaptive Fuzzy Extended Kalman Filter

为准确实时地获取车辆运动状态信息,满足车辆主动安全控制系统的需求,基于模糊控制器和扩展卡尔曼滤波(EKF)算法,采用非线性3自由度车辆动力学模型,提出一种基于自适应模糊扩展卡尔曼滤波(AFEKF)的车辆运动状态联合估计策略。首先利用EKF算法对待测量噪声的输入量联合估计得到所需的状态量,然后建立模糊控制器对其进行自适应调节,最后应用MATLAB/Simulink仿真平台建立14自由度车辆动力学模型对估计算法进行仿真和实车试验验证。结果表明:AFEKF算法能够准确有效地估计车辆的行驶状态,且与EKF算法相比,准确性和鲁棒性更好。

In order to accurately obtain vehicle motion status information in real time and meet the requirements of active safety control system, this paper proposes a strategy for joint estimation of vehicle dynamic state based on Adaptive Fuzzy Extended Kalman Filter (AFEKF). The estimation strategy uses a nonlinear 3-degree-of-freedom vehicle dynamic model, and based on fuzzy controller and Extended Kalman Filter (EKF) algorithm. Firstly, the EKF algorithm is used to jointly estimate the required state quantity for the input quantity with measurement noise. Secondly, a fuzzy controller is established to adaptively adjust EKF. Finally, MATLAB/Simulink simulation platform is used to establish a 14-degree-offreedom vehicle dynamics model to verify the estimation algorithm with simulation and real vehicle test. The experimental results show that the AFEKF algorithm can accurately and effectively estimate the driving state of the vehicle, and compared with the EKF algorithm, AFEKF algorithm has better accuracy and robustness.