Path-Following based on Nonlinear Model Predictive Control with Adaptive Path Preview

Abstract

This paper presents a Nonlinear Model Predictive Controller (NMPC) for the path-following of autonomous vehicles and an algorithm to adaptively adjust the preview distance. The prediction model includes vehicle dynamics, path-following dynamics, and system input dynamics. The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics, as well as nonlinear tire forces. The tracking error dynamics are derived based on the curvilinear coordinates. The cost function is designed to minimize path-tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits. An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed. The length of the preview path is adaptively adjusted based on the vehicle speed, heading error, and path curvature. We validate the controller performance in a simulation environment with the autonomous racing scenario. The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors. The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.

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