In order to eliminate the coupling effect between vertical and horizontal directions of vehicle systems,neural network inverse system decoupling control is carried out for vehicle dynamics model.The research object is driverless vehicle with four-wheel drive and front wheel steering.Firstly,the vehicle dynamics model with two de-grees of freedom including lateral motion and yaw motion is analyzed by interactor algorithm,Secondly,the inverse system structures of Convolutional Neural Networks(CNN)and Long Short Term Memory(LSTM)neural net-works are built to replace the traditional inverse system decoupling strategy,and the feasibility of this method is ver-ified,The decoupling method is applied to the trajectory tracking control design of driverless vehicle.The tracking effect is judged by observing the output response curve of the vehicle,and then the feasibility and stability of the method are proved,Finally,through the joint simulation test of CarSim and MATLAB/Simulink,it is proved that the CNN +LSTM neural network inverse system decoupling control designed in this paper has good tracking charac-teristics and stability under various working conditions.
关键词
无人驾驶车辆/逆系统解耦/CNN+LSTM神经网络/轨迹跟踪
Key words
driverless vehicle/inverse system decoupling/CNN +LSTM neural network/track tracking
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