Research on trajectory tracking control of unmanned vehicle based on PSO_PID

Abstract To enhance the tracking accuracy and stability of unmanned vehicles in trajectory tracking control, a particle swarm optimized proportional-integral-derivative (PID) control mixed with model predictive control is established. First, the model predictive controller is generated based on the vehicle dynamics model to calculate the lateral error; secondly, the particle swarm optimized PID controller is used to compensate the front wheel angle to reduce the steady-state lateral error; finally, the joint simulation analysis is carried out. The results of the study show that the MPC control incorporating PSO_PID exhibits a significant improvement in the lateral position error compared to the conventional MPC control, reducing the error by 65.51% and effectively improving the tracking accuracy. In addition, the method also successfully reduces the peak yaw angle error by 57.89% and the peak fluctuation amplitude of the vehicle's front wheel angle by 16.33%, thus significantly improving the stability of trajectory tracking.