A Comparison Study of Kinematic and Dynamic Models for Trajectory Tracking of Autonomous Vehicles Using Model Predictive Control

Efficient trajectory tracking approaches can enable autonomous vehicles not only to get a smooth trajectory but to achieve a lower energy dissipation. Since vehicle model plays an important role in trajectory tracking, this paper investigates and compares the performance of two classical vehicle models for trajectory tracking of autonomous vehicles using model predictive control (MPC). Firstly, a two-degree-of-freedom kinematic model and a three-degree-of-freedom yaw dynamic model are established for autonomous vehicles. Meanwhile, in order to carry out tracking control more effectively and smoothly, the tire slip angle has been taken into account by the dynamic model. Then, we design two MPC controllers for trajectory tracking, which are based on the kinematic model and the dynamic model, respectively. The performances of two MPC controllers are evaluated and compared on the Carsim/Matlab joint simulation platform. Experimental results demonstrated that, under low speed working conditions, both two MPC controllers can follow the reference trajectory with high accuracy and stability. However, under high speed working conditions, the tracking error of the kinematic model is too large to be used in the real trajectory tracking problem. On the contrary, the controller based on the dynamic model still performs a good tracking effect. In addition, this study offers a guidance on how to select a suitable vehicle model for autonomous vehicles under different speed working conditions.