| 基于EKF和RBF的路面附着系数估计 |
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| 引用本文： | 查云飞,刘鑫烨,马芳武,吕小龙.基于EKF和RBF的路面附着系数估计[J].福建工程学院学报,2022,0(1):1-6+34. |
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| 作者姓名： | 查云飞 刘鑫烨 马芳武 吕小龙 |
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| 作者单位： | 福建工程学院福建省汽车电子与电驱动技术重点实验室 |
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| 摘 要： | 针对车辆行驶下的路面附着系数估计问题，提出了扩展卡尔曼滤波算法(EKF，Extended Kalman Filter)与径向基神经网络(RBF，Radial Basis Functionneural network)相融合。通过扩展卡尔曼滤波算法得出路面附着系数估计所需要的车辆状态参数，结合轮速等直接数据采用径向基神经网络对路面附着系数进行估计。神经网络的训练样本通过Carsim/Simulink收集不同行驶工况，并采用差值寻优的方法对径向基神经网络算法中的决定系数进行优化。基于双移线工况验证了该算法在路面附着系数估计上具有较高的精准度。
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| 关 键 词： | 路面附着系数 算法融合 扩展卡尔曼滤波 径向基神经网络 决定系数优化 |
Estimation of road adhesion coefficient based on EKF and RBF |
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| ZHA Yunfei,LIU Xinye,MA Fangwu,LYV Xiaolong.Estimation of road adhesion coefficient based on EKF and RBF[J].Journal of Fujian University of Technology,2022,0(1):1-6+34. |
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| Authors: | ZHA Yunfei LIU Xinye MA Fangwu LYV Xiaolong |
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| Institution: | Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Technology, Fujian University of Technology |
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| Abstract: | Aiming at the problem of road adhesion coefficient estimation under vehicle driving, a fusion algorithm is proposed integrating extended Kalman filter and radial basis function neural network. The vehicle state parameters needed to estimate the road adhesion coefficient were obtained by the extended Kalman filter algorithm. Combined with the direct data such as wheel speed, the road adhesion coefficient was estimated by radial basis function neural network. Training samples of neural networks were collected by Carsim/Simulink in different driving conditions, and the decision coefficients of radial basis neural network algorithm were optimized by the method of difference optimization. Finally, the accuracy of the algorithm was verified based on double lane change conditions. |
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| Keywords: | road adhesion coefficient algorithm fusion extended Kalman filter radial basis function neural network determination coefficient optimization |
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