A Nonlinear Model Predictive Control for Automated Drifting with a Standard Passenger Vehicle

TL;DR

This paper introduces a nonlinear model predictive control method for automated drifting in standard passenger vehicles, enabling stable high sideslip angles and precise path following in various friction conditions.

Contribution

The paper proposes a novel control architecture combining offline equilibrium maps, predictive control, and path-following for automated drifting in standard vehicles.

Findings

Successfully stabilizes vehicle at 30° sideslip angle

Maintains lateral deviation within 1 meter during drifting

Effective in both high and low friction conditions

Abstract

This paper presents a novel approach to automated drifting with a standard passenger vehicle, which involves a Nonlinear Model Predictive Control to stabilise and maintain the vehicle at high sideslip angle conditions. The proposed controller architecture is split into three components. The first part consists of the offline computed equilibrium maps, which provide the equilibrium points for each vehicle state given the desired sideslip angle and radius of the path. The second is the predictive controller minimising the errors between the equilibrium and actual vehicle states. The third is a path-following controller, which reduces the path error, altering the equilibrium curvature path. In a high-fidelity simulation environment, we validate the controller architecture capacity to stabilise the vehicle in automated drifting along a desired path, with a maximal lateral path deviation of 1 m. In the experiments with a standard passenger vehicle, we demonstrate that the proposed approach is capable of bringing and maintaining the vehicle at the desired 30 deg sideslip angle in both high and low friction conditions.