Improved Adaptive Path Following Control System for Autonomous Vehicle in Different Velocities

Abstract

Path following is the basic technology of the autonomous vehicle (AV), many preview control methods have been widely applied to path following tasks. However, less of them take the variable vehicle velocities into account. In fact, the velocity is an important factor affecting the tracking accuracy. Especially, when an AV is in high velocity, it is not easy to achieve path following with high accuracy. To improve the adaptivity of path following in different velocities, an improved adaptive path following control system (PFCS) constructed by a course angle optimal referential model (CAORM) and a model predictive controller (MPC) is developed in this paper. The CAORM can provide the referential course angle, according to the vehicle longitudinal and lateral velocities, which significantly improves the adaptivity of the proposed PFCS in different velocities. And the CAORM is mainly implemented by a fuzzy inference system and a novel preview model, using human driving experience. The MPC is applied to realize the course control with high accuracy via manipulating the steering angle. Finally, the tracking performance of the PFCS is verified via simulation experiments on the Simulink-CarSim platform.