汽车半主动空气悬架的神经网络控制方法

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。

Neural network control method of automotive semi-active air suspension

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.3 tabs,5 figs,15 refs.