Autonomous vehicle trajectory tracking control utilizing front-wheel steering angle compensation

To address the challenges of low path-tracking accuracy and poor driving smoothness in autonomous vehicles under various operating situations, a controller for tracking paths was developed where combining model predictive control (MPC) controller with front wheel angle adjusted fuzzy control. Initially, the prediction model is built using the vehicle dynamics model, and the appropriate cost function is chosen to perform the optimization process. Secondly, a vehicle-path error relationship model is constructed, and the corner compensation fuzzy controller is designed with the vehicle's yaw angle error and lateral deviation as inputs. The front wheel angle is then adjusted using a fuzzy control method to achieve optimal tracking control. Ultimately, bying CarSim/Matlab simulations, the designed controller is compared to the MPC controller under various operating conditions. The results show that the designed controller's lateral deviation of trajectory tracking improves by 46.4% and 40.3% at low and high speeds, respectively, which improves the tracking accuracy to some extent.