Comparative Study of Prediction Models for Model Predictive Path- Tracking Control in Wide Driving Speed Range

This study compares and evaluates the effect of the choice of the vehicle's prediction model on the performance in designing a path-tracking controller for vehicles using Model Predictive Control (MPC). The Kinematic Ackermann Model (KAM), the Kinematic Bicycle Model (KBM), and the Dynamic Bicycle Model (DBM) are well known as nonlinear prediction models. The stability and tracking performance of these models are evaluated using simulations, and a newly proposed DBM improved in Low-speed range (DBM-L) is also compared. As a result of the simulation, the proposed DBM-L was able to run in the widest 0 to 120km/h speed range among the models tested, and it was able to achieve the stop-and-go behavior that was not possible with the conventional DBM. In the future, if we can solve the problem that the tracking accuracy of the DBM-L is slightly decreased in the extremely low and high speed ranges, a vehicle prediction model that can be used in all speed ranges is expected to be realized.