基于改进BP神经网络的智能车纵向控制方法

doi:10.19562/j.chinasae.qcgc.2022.08.006

Abstract

Abstract:

For the problem that the parameters of traditional PI control are fixed and difficult to be adjusted in the process of vehicle speed tracking， a longitudinal control method of intelligent vehicle based on the improved BP neural network is proposed. The BP neural network in drive mode and brake mode is established respectively. In view of the difficulty in selecting initial parameters of BP neural network and the problem of gradient disappearing in reverse self-learning， particle swarm optimization and batch normalization are used to improve the BP neural network. Finally， the dynamic self-tuning of PI parameters is realized. By Carsim/Simulink co-simulation and real vehicle test， the proposed method is verified. The results show that the proposed longitudinal control method can realize rapid adjustment of parameters based on error and improve the longitudinal control accuracy of the vehicle compared with the traditional PI control.

Key words: intelligent vehicle, speed tracking control, BP neural network, self-tuning, particle swarm optimization

Wang Liang,Zhaobo Qin,Liang Chen,Yougang Bian,Manjiang Hu. Longitudinal Control Method of Intelligent Vehicles Based on the Improved BP Neural Network[J].Automotive Engineering, 2022, 44(8): 1162-1172.

Figures/Tables 11

参数	数值
采样周期 $d t$ /s	0.02
预瞄时间常数（PI） $Δ t 1$ /s	0.2
预瞄时间常数（BP） $Δ t 2$ /s	0.2
最小预瞄距离 $d m i n$ /m	0.5
模式切换死区偏置量 $Δ v$ /（m·s^-1）	0.3
线性整流激活函数斜率 $k$	1/5.5
驱动BP输出增益 $G a i n 1,2$	［20，1］
制动BP输出增益 $G a i n 1,2$	［13，1］
驱动/制动BP网络学习速率 $η$	0.002、0.002
驱动/制动BP网络动量因子 $α$	0.001、0.001
批处理归一化所用数据量 $h$	10、10

参数	数值
采样周期 $d t$ /s	0.02
预瞄时间常数（PI） $Δ t 1$ /s	1.0
预瞄时间常数（BP） $Δ t 2$ /s	0.2
最小预瞄距离 $d m i n$ /m	2.5
模式切换死区偏置量 $Δ v$ /（m·s^-1）	0.5
线性整流激活函数斜率 $k$	1/5.5
驱动BP输出增益 $G a i n 1,2$	［20，3］
制动BP输出增益 $G a i n 1,2$	［13，3］
驱动/制动BP网络学习速率 $η$	0.002、0.002
驱动/制动BP网络动量因子 $α$	0.001、0.001
批处理归一化所用数据量 $h$	10、10

References 19

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测试工况	控制器类型	最大误差/（m·s^-1）	均方误差/ （m·s^-1）²
A	改进BP-PI	0.713	0.059
A	常规PI	1.131	0.355
B	改进BP-PI	1.233	0.167
B	常规PI	1.406	0.384

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Longitudinal Control Method of Intelligent Vehicles Based on the Improved BP Neural Network

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Figures/Tables 11

References 19

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