An MPC trajectory tracking strategy for autonomous vehicles considering actuator hard constraints

Abstract

In response to the problems of insufficient trajectory tracking accuracy for autonomous vehicles in complex road conditions and the tendency of traditional algorithms to cause uncontrollable overshoots, this paper proposes a closed-loop tracking strategy based on model predictive control technology. This strategy first establishes a vehicle kinematics single-vehicle model and derives the discrete state space equations; then it constructs a quadratic programming cost function that includes control increment penalties and hard constraints on the front wheel rotation angle of ±45 degrees. The simulation results of the dual-tracking line operation show that, compared with the traditional pure tracking algorithm, this strategy reduces the maximum lateral error from 0.31m to within 0.06m, a reduction of approximately 81%; the maximum heading angle deviation is controlled within 0.04rad, and the steering command is smooth without touching the physical boundaries. This study further verified the effectiveness of incorporating physical hard constraints into the trajectory tracking solution framework, providing a useful algorithm reference for the anti-saturation design and smoothness development of the chassis line control system in intelligent connected vehicles.