Abstract:

A trajectory tracking control algorithm integrating fuzzy control strategy with time-domain adaptive adjustment model predictive control (MPC) was proposed. to address the issue that road curvature and vehicle speed information are usually not considered in autonomous vehicle trajectory tracking control, and to suppress lateral deviations during vehicle trajectory tracking while enhancing the anti-interference ability of the control system, A vehicle kinematic model and a model predictive controller were established, different speed conditions were designed, road curvature and desired vehicle speed were taken as fuzzy control inputs, and the prediction horizon parameters of the MPC algorithm were optimized via the fuzzy controller. Joint simulations using Carsim and Simulink were carried out to implement trajectory tracking control at different speeds on two trajectories with distinct curvatures. The results show that, in the double lane change scenario, compared with the fixed-horizon controller and linear quadratic regulator (LQR), the adaptive time-domain MPC controller achieves a maximum reduction of 85.81% and 78.86% in lateral errors at low speed (30 km/h) and high speed (90 km/h) respectively; in the multi-curve scenario, it realizes a maximum reduction of 96.32% and 86.4% in lateral errors at low speed and high speed respectively. These findings confirm that the proposed control strategy can significantly improve the system's tracking performance, effectively reduce trajectory deviations and maintain the dynamic stability of the vehicle under different speed conditions.

Key words: autonomous driving, trajectory tracking, adaptive, model predictive control (MPC)