Path Tracking of Autonomous Vehicle Based on NMPC With Pre-Steering

Abstract

In scenarios characterized by significant curvature, such as curves, the presence of steering system hysteresis and linearization of the vehicle model may lead to understeer and steady-state errors, thereby exerting an adverse impact on the performance of the tracking control system. To address these formidable challenges, this study introduces an innovative path planning and tracking framework. This framework leverages batch informed trees to derive the reference path and employs Nonlinear Model Predictive Control (NMPC) with pre-steering to accomplish efficient tracking of the predetermined path. Additionally, a smart car experiment platform has been established for simulating and validating various scenarios, with results demonstrating improved performance in tracking accuracy and steering smoothness when compared to Traditional Model Predictive Control (TMPC). Particularly in scenarios involving substantial curvature, such as curves, the proposed framework exhibits a nearly 50% reduction in root mean square error, underscoring its enhanced performance.