Model-Free Vehicle Rollover Prevention: A Data-Driven Predictive Control Approach

Abstract Vehicle rollovers pose a significant safety risk and account for a disproportionately high number of fatalities in road accidents. This paper addresses the challenge of rollover prevention using data-enabled predictive control (DeePC), a data-driven control strategy that directly leverages raw input–output data to maintain vehicle stability without requiring explicit system modeling. To enhance computational efficiency, we employ a reduced-dimension DeePC (RD-DeePC) that utilizes singular value decomposition (SVD)-based dimension reduction to significantly lower computation complexity without compromising control performance. This optimization enables real-time application in scenarios with high-dimensional data, making the approach more practical for deployment in real-world vehicles. The proposed approach is validated through high-fidelity carsim simulations in both sedan and utility truck scenarios, demonstrating its versatility and ability to maintain vehicle stability under challenging driving conditions. Comparative results with linear model predictive control (LMPC) highlight the superior performance of DeePC in preventing rollovers while preserving maneuverability. The findings suggest that DeePC offers a robust and adaptable solution for rollover prevention, capable of handling varying road and vehicle conditions.