基于卷积神经网络的路面识别及半主动悬架控制

Road Identification and Semi-active Suspension Control Based on Convolutional Neural Network

路面对车辆的平顺性、操纵稳定性有直接影响,实时获取路面信息对提升车辆性能具有重要意义。针对传统路面识别方法中难以精确识别多种路面类型的问题,采用卷积神经网络对路面类型进行识别,并根据不同路面输入下悬架系统的输出响应来调整控制器参数,使可控悬架在不同路面下均保持最优性能。建立车辆1/4半主动悬架模型;搭建卷积神经网络基本结构并通过所采集的4种典型城市和非城市路面图像对网络进行训练以及测试;采用遗传算法求取沥青路、弹石路、砂石路、水泥路4种不同路面激励下悬架的最优控制参数;根据路面识别结果及优化结果实现悬架控制参数的自适应调整。仿真结果表明:基于卷积神经网络的路面识别方法能够对多种路面进行准确识别;基于路面识别和遗传算法的半主动悬架控制系统可根据不同路面类型自适应调整悬架参数,有效提升车辆性能。

Road has a direct impact on vehicle ride comfort and handling stability,so that the real-time acquisition of road information plays an important role in improving the vehicle performance.The multiple types of road are difficultly identified accurately using traditional road identification methods.The convolutional neural network is used to identify the road type,and then the identified road type is used as the basis for tuning the controller parameters of suspension system in order to make the controllable suspension system maintain the optimal performance under different road surfaces.Firstly,the quarter-vehicle semi-active suspension model is established.Secondly,the basic structure of convolutional neural network is built,and this network is trained and tested based on four typical urban and non-urban road images collected in advance.And then genetic algorithm is used to obtain the optimal control parameters of suspension system under excitations of four different roads,such as asphalt road,sandstone road,pebble road and cement road.Finally,the suspension control parameters are adaptively adjusted according to both the identified and optimized results of road surface.The simulated results show that the road identification method based on convolutional neural network can accurately identify a variety of roads;the semi-active suspension control system based on road identification and genetic algorithm can adaptively adjust the suspension parameters according to different road surfaces,thus improving the vehicle performance effectively.