汽车半主动空气悬架的神经网络控制方法

朱思洪; 吕宝占; 王辉; 张莹; 贺亮

汽车半主动空气悬架的神经网络控制方法

南京农业大学工学院, 江苏南京 210031

基金项目:

教育部优秀青年教师计划项目 2001-1829

详细信息

作者简介:
朱思洪(1962-), 男, 湖北监利人, 南京农业大学教授, 工学博士, 从事机械现代设计理论及方法与车辆振动及控制研究

中图分类号: U463.33
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- 文章访问数: 319
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- 被引次数: 0
出版历程
- 收稿日期: 2006-06-22
- 刊出日期: 2006-12-25

Neural network control method of automotive semi-active air suspension

School of Engineering, Nanjing Agricultural University, Nanjing 210031, Jiangsu, China

More Information

Author Bio:
Zhu Si-hong(1962-), male, PhD, professor, 86-25-58606603, zsh@njau.edu.cn

摘要

摘要: 为了提高汽车半主动悬架的控制效果, 以空气弹簧压力为控制对象, 应用自适应神经网络控制方法, 进行了不同路面激励下的半主动空气悬架的车身垂直加速度、悬架动挠度和车轮动载荷的计算机仿真和实验研究, 并与被动悬架系统的相应参数进行了对比。发现在白噪声路面和较低频率的正弦路面激励下, 半主动空气悬架采用自适应神经网络控制能够明显降低车身垂直加速度、车轮动载荷和悬架动挠度, 降低范围为16%~85%, 提高了车辆的操纵稳定性, 改善了车辆的行驶安全性与乘坐舒适性。
- 车辆工程 /
- 空气悬架 /
- 神经网络控制方法 /
- 仿真 /
- 实验研究
Abstract: In order to improve the control effect of vehicle semi-active suspension, adaptive neural network control method was developed, the air pressure of spring was taken as controlled object, the computer simulation and experiment of body plumb acceleration, suspension dynamic deflection and wheel dynamic load with semi-active air suspension under the excitations of different road surfaces were studied, the control result of neural network control suspension was compared with the control effect of passive suspension.Under the excitations of white noise roads and lower frequency sinusoid roads, the result shows that the semi-active suspension with the method not only markedly reduces body plumb acceleration, wheel dynamic load, suspension dynamic deflection, the decreased range is from 16% to 85%, but also improves automotive driving stability, riding comfortable performance and running security.
- vehicle engineering /
- air suspension /
- neural network control method /
- simulation /
- experimental study

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图 1 悬架结构与数学模型

Figure 1. Structure and mathematical model of suspension

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图 2 控制结构

Figure 2. Control structure

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图 3 神经网络辨识器结构

Figure 3. Identification structure of neural network

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图 4 悬架系统的动态响应

Figure 4. Dynamic response of suspension system

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图 5 车身加速度对比

Figure 5. Comparison of body accelerations

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表 1 悬架参数

Table 1. Suspension parameters

簧下质量m₁/kg	簧上质量m₂/kg	轮胎刚度k₁/ (kN·m^-1)	悬架基值刚度k₀/ (kN·m^-1)	悬架阻尼c/ (kN·s·m^-1)
25	330	170	13	1

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表 2 仿真结果的均方根值

Table 2. Mean square roots of simulation result

悬架类型	车身加速度/ (m·s^-2)		车轮动载荷/N		悬架动挠度/mm
悬架类型	白噪声	正弦波	白噪声	正弦波	白噪声	正弦波
被动悬架	0.282 9	1.899 2	102.213 4	621.805 7	8.7	23.2
神经网络控制悬架	0.088 3	0.283 9	70.309 6	525.168 5	5.4	18.1

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表 3 车身加速度均方根值

Table 3. Mean square roots of body accelerations m·s^-2

悬架类型	正弦波激励频率f₀/Hz
悬架类型	4	6	8
被动悬架	4.652 7	1.124 3	1.059 4
神经网络控制悬架	2.252 6	0.810 0	0.706 8

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