A Novel Enhanced Data-Driven Model-Free Adaptive Control Scheme for Path Tracking of Autonomous Vehicles
Shida Liu, Guang Lin, Honghai Ji, Shangtai Jin, Zhongsheng Hou
Abstract
In this paper, an enhanced model-free adaptive control algorithm considering time delay is proposed for the path tracking problem of autonomous vehicles. First, a path tracking mechanism based on the preview-deviation-yaw angle is proposed, which transforms the path tracking problem into a control problem of the preview-deviation-yaw angle. A novel partial form dynamic linearization (PFDL) technique is then employed to transform the vehicle dynamic models into a discrete-time data model with a time-varying pseudogradient (PG), and the proposed controller (PFDL-EMFAC) is designed based on this data model. Moreover, a compensation mechanism is designed for the system time delay by combining the Smith predictor and tracking differentiator (TD). Notably, implementing the controller does not involve any model information; it is a purely data-driven control method. Furthermore, the convergence of the proposed controller is proven via mathematical analysis. The validity of the proposed controller was validated through CarSim-MATLAB cosimulation, and its applicability was verified via the Ankai HFF6668GEV1 autonomous driving platform on a test road in Hefei, China.