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

TL;DR

The paper addresses automated drifting of a standard passenger vehicle by developing a nonlinear model predictive control framework based on a nonlinear single-track model with Pacejka tyres. It combines offline equilibrium maps, an NMPC that stabilizes the vehicle at high sideslip while tracking a reference path, and a path-following module that modulates curvature to reduce lateral error, implemented with ACADO at 50 Hz and horizon $N=25$. In high-fidelity BMW simulations, the approach stabilizes drifting and tracks the path, though real-world validation reveals steering-torque limitations of production steering hardware that prevent full automation; a semi-automated drifting setup demonstrates feasibility and seals the practical path toward fully autonomous drifting with appropriate actuation. Overall, the work shows real-time NMPC can control drifting dynamics on production vehicles, while highlighting hardware constraints as a key enabler for full automation.

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.

Figures (9)

• Figure 1: BMW M3 Competition in automated drifting.
• Figure 2: Steady-state drifting equilibria maps.
• Figure 3: Comparison of the computed and measured vehicle states in equilibrium.
• Figure 4: Architecture of the proposed controller.
• Figure 5: Vehicle states in the alternating drifting manoeuvre on a high friction surface.