Yaw stability control of distributed drive electric vehicle based on neural network

In order to improve the stability control ability of distributed electric drive vehicles during driving, a novel non-singular fast terminal sliding film control method is proposed. This method integrates the fractional-order control law of the fractional-order non-singular fast terminal sliding film and utilizes the radial basis function neural network to model the entire system and its uncertain components with approximation. By adjusting the control reaching law of the sliding film controller to achieve gain tuning, the dependence of the traditional sliding mode control on the preset information of the system disturbance is improved and reduced, so that the strong robustness of the system is enhanced. Then, the energy rollover index (ERI) is used as the warning index, and the above improved sliding mode controller is used to maintain stability. Through the implementation of a torque distribution strategy, the output yaw torque is distributed to each wheel, so as to complete the stability control of the vehicle. The simulation experiment is conducted through co-simulation of CARSIM and MATLAB/Simulink to verify the effectiveness of the controller.