Robust Nonlinear Trajectory Tracking Control for Autonomous Racing on Three-Dimensional Tracks

TL;DR

This paper introduces a robust nonlinear MPC scheme for autonomous vehicle trajectory tracking on 3D tracks, accounting for terrain effects and uncertainties to enhance accuracy and stability.

Contribution

A novel 3D dynamic single-track MPC model with uncertainty-aware constraint tightening for improved autonomous vehicle control at handling limits.

Findings

Improved trajectory-tracking accuracy in simulations.

Maintains low computation times despite complex modeling.

Effectively handles terrain-induced uncertainties.

Abstract

We propose a robust nonlinear model predictive control (MPC) scheme for trajectory-tracking control of autonomous vehicles at the limits of handling on non-planar road surfaces. We derive the dynamics from first principles and selectively omit terms with negligible dynamic influence to maintain real-time capability. The resulting MPC with a three-dimensional (3D) dynamic single-track model integrates relevant dynamic effects directly into the prediction model and leverages them to improve prediction accuracy and therefore control performance. Even if the influence of terrain-induced vertical loads on the total acceleration potential is modeled, tire-road interactions are subject to uncertainty and disturbance. The uncertainty-aware constraint tightening scheme introduces a margin to constraint bounds to keep the vehicle controllable and stable in this environment. To validate our proposed approach, we perform high-fidelity dynamic double-track vehicle dynamics simulations on a model of a real circuit. We find that our algorithm can improve trajectory-tracking accuracy while maintaining low computation times.