UT-MPC: Manifold-Based Model Predictive Control with Dynamic Weighting and Feedback for Vehicle Trajectory Tracking on Uneven Terrain

As autonomous vehicle (AV) technology advance, their ability to navigate uneven terrains, such as mountainous areas, becomes increasingly important. However, current trajectory tracking methods struggle with tracking accuracy and stability due to insufficient consideration of terrain slopes and vehicle kinematics. In this paper, we propose a Manifold-Based Model Predictive Control framework designed for Ackermann steering electrified vehicles on uneven terrains. This method models the trajectory tracking control on manifolds, utilizing acceleration and front-wheel steering angle as control inputs to enhance control stability. To mitigate model inaccuracies and enhance the controller’s adaptability, we dynamically adjust the controller’s objective function weights based on trajectory curvature and integrate a PID feedback mechanism to provide real-time compensation for vehicle speed and steering angle. When fitting the surface terrain equation, we select sparse key points along the reference trajectory, achieving lightweight computation while maintaining high fitting accuracy. The experimental and simulation results demonstrate that the proposed UT-MPC controller improves tracking performance by 53.74% and 42.68% compared to four baseline methods when tracking a trajectory on different uneven terrain maps. Real-world experiments have also demonstrated the effectiveness of UT-MPC.