Research on trajectory tracking control based on radial basis neural network PID and model predictive control

In order to improve the stability and robustness of self-driving vehicle, a trajectory tracking control method was proposed based on the combination of self-adaptive proportional integral derivative (PID) of radial basis function neural network (RBFNN−PID) and model predictive control (MPC). A simulation model of intelligent vehicle longitudinal speed control and lateral control was established based on self-adaptive RBFNN−PID algorithm, MPC algorithm and vehicle dynamics model. Based on that, the lateral MPC and LQR−PID control algorithm were used as benchmarks to demonstrate the superiority of the presented control method in trajectory tracking. The simulation results show that the presented control method has higher accuracy than the comparing group. Finally, the hardware-in-the-loop verification of the proposed control method is carried out. The results show that the proposed trajectory tracking control algorithm is effective and advanced in trajectory tracking accuracy and stability.