Path tracking control of intelligent vehicle based on fuzzy neural network

In order to improve the autonomous path tracking control accuracy of intelligent vehicle, an intelligent path tracking control strategy is proposed based on fuzzy neural network control and neural network prediction. The inputs of steering controller are the transverse path tracking error at preview points and the yaw rate and lateral acceleration of vehicle, while those for speed controller are the area error at preview points and the lateral acceleration, side slip angle and steering wheel angle of vehicle, and error back propagation technique is adopted for network training. The results of simulation and test show that through the training of driver operation samples, the path tracking controller designed can realize speed and steering control of intelligent vehicle with relatively desirable transverse path tracking error and target speed.