Trajectory Tracking Control Design for Driverless Racing Car Considering Longitudinal Load Transfer

Driverless racing car requires accurate trajectory tracking for fastest driving. However, the extreme driving conditions make it difficult to achieve the accurate tracking performance while meeting the real-time control requirement. Therefore, this paper proposed a trajectory tracking controller based on nonlinear model predictive control (MPC) considering longitudinal load transfer. First, the vehicle dynamic model, including the load transfer between front and rear axles was built. Second, the optimal tracking problem is formulated to minimize the lateral and speed tracking errors while ensuring vehicle stability. In addition, the speed terminal constraint is added to prevent the runaway situation caused by small visual range. Finally, the driverless racing car is simulated in a track to follow a per-defined trajectory. By comparing to a QP-MPC controller, the proposed NMPC method improves the lap time by 1% while ensuring vehicle stability.