Nonlinear Model Predictive Control for Path Tracking Using Discrete Previewed Points

Abstract

In this paper, a nonlinear model predictive control (NMPC) scheme for path tracking of autonomous vehicles using discrete previewed points in the inertial coordinate is presented. The control objective is to improve the tracking accuracy under small lateral acceleration in the scenario of low speed and narrow space. The path tracking problem is formulated as a nonlinear model predictive control model, in which the vertical distance between the vehicle position and the tangent of previewed point is adopted to evaluate the tracking error. The discrete previewed points are generated from the path points independently, which does not rely on the approximate path functions. The iterative initial values of the optimization model are appropriately selected by combining with the Stanley method to accelerate optimization computations. A simulation comparison between the proposed NMPC controller and a linear model predictive control (LMPC) controller is conducted through Carsim-Matlab/Simulink co-simulations. Simulation results show that the proposed controller exhibits better tracking accuracy than the LMPC controller under small lateral acceleration, especially when the path curvature is large and continuously changing.