Fuzzy neural network simulation of vehicle yaw rate control based on PID

The core of driving force control strategy is the research core of electric vehicle driven by wheel motor. In order to give full play to the advantages of wheel motor drive system, a reasonable control strategy must be designed. In this paper, the stability of the vehicle is determined based on the sideslip angle of the center of mass of the vehicle. Through the speed and steering wheel angle, the actual yaw rate of the vehicle at a certain speed and the theoretical yaw rate at this speed are determined as the control objectives. The PID control is used to distribute the four-wheel brake pressure, generate additional yaw moment to adjust the vehicle attitude; and the traditional PID control is used to control the vehicle attitude. In contrast, fuzzy control using human expert control experience, for the control of nonlinear and complex objects shows the advantages of good robustness and high control performance. Therefore, on the basis of PID, through neural network learning, fuzzy neural network is used to optimize the output fuzzy rules and membership function to control the vehicle. The whole process is carried out with CarSim and Simulink Simulation is carried out to verify the control optimization process.