基于神经网络的无人驾驶车辆转向控制研究
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摘要
为了提高无人驾驶车辆进行路径跟踪时转向的准确性,基于神经网络控制理论,利用ADAMS/Car与MATLAB/Simulink进行无人驾驶车辆转向控制联合仿真。利用ADAMS/Car模块建立整车模型,进行规定路径下的跟踪实验并收集路径、车速、前轮转角等信息,以作为神经网络的训练样本。利用MATLAB对训练样本进行训练,并在Simulink中建立神经网络控制器。最后利用ADAMS/Control模块连接ADAMS/Car与MATLAB/Simulink,实现无人驾驶车辆路径跟踪时转向控制的联合仿真。仿真分析结果表明:所建立的神经网络转向控制器能够对路径进行良好的跟踪且具有良好的鲁棒性;同时验证了联合仿真的可行性与优越性,为智能车辆的整车开发提供了思路。
In order to improve the aecuracy of steering in path tracking of unmanned vehicle, the co-simulation of steering control of unmanned vehicle is carried out by using ADAMS/Car and MATLAB/Simulink, based on neural network control theory. The vehicle model is built by ADAMS/Car module, and the tracking experiments under the prescribed path are carried out, then, information such as path,speed and front wheel corner is collected as training samples of neural network. The training samples are trained by MATLAB, and the neural network controller is established in Simulink. Finally, the ADAMS/Control module is used to connect ADAMS/Car and MATLAB/Simulink to realize co-simulation of steering control for unmanned vehicle path tracking. The simulation results show that the established neural network steering controller with good robustness can track the path well. At the same time, the feasibility and superiority of the cosimulation are verified, which provide a thought for the development of intelligent vehicle.
In order to improve the aecuracy of steering in path tracking of unmanned vehicle, the co-simulation of steering control of unmanned vehicle is carried out by using ADAMS/Car and MATLAB/Simulink, based on neural network control theory. The vehicle model is built by ADAMS/Car module, and the tracking experiments under the prescribed path are carried out, then, information such as path,speed and front wheel corner is collected as training samples of neural network. The training samples are trained by MATLAB, and the neural network controller is established in Simulink. Finally, the ADAMS/Control module is used to connect ADAMS/Car and MATLAB/Simulink to realize co-simulation of steering control for unmanned vehicle path tracking. The simulation results show that the established neural network steering controller with good robustness can track the path well. At the same time, the feasibility and superiority of the cosimulation are verified, which provide a thought for the development of intelligent vehicle.
引文
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[10]公路工程技术标准:JTG BO1-2014[S], 2014.
[2]熊波,曲仕茹.基于模糊控制的智能车辆自主行驶方法研究[J].交通运输系统工程与信息,2010,10(2):70-75.
[3]秦万军,徐友春,李明喜,等.基于二自由度模型的无人驾驶车辆轨迹跟踪控制研究[J].军事交通学院学报,2014,16(11):31-35.
[4]飞思科技产品研发中心.神经网络理论与MATLAB 7实现[M].北京:电子工业出版社,2005.
[5]陈军.MSC. AD AMS技术与工程分析实例[M].北京:中国水利水电出版社,2008.
[6]周红妮,袁浩文.基于MATLAB/Simulink与ADAMS/Car的车辆稳定性控制系统联合仿真[J].湖北汽车工业学院学报,2012,26(4):20-23.
[7]茅峻杰.基于ADAMS与MATLAB/Simulink的四轮转向车辆建模与模糊控制研究[D].江苏大学,2012.
[8]王文成.神经网络及其在汽车工程中的应用[M].北京:北京理工大学出版社,1998.
[9]岳本龙.基于ADAMS和Simulink的神经网络控制4WS汽车联合仿真研究[D].沈阳:东北大学,2008.
[10]公路工程技术标准:JTG BO1-2014[S], 2014.