Optimal Rollover Prevention With Steer by Wire and Differential Braking

TLDR

The benchmark control law assumes full knowledge of the input trajectory, establishing the best achievable performance for the vehicle roll‑mode system. The study aims to develop a Model Predictive Control framework for automobile stability control. Using MPC, the authors design a roll‑mode controller that actively limits peak roll angle while tracking the driver’s yaw‑rate command, first assuming full input knowledge and then relaxing to only the current steering command. Numerical simulations on a nonlinear vehicle model demonstrate that the controller tracks the intended yaw rate during extreme maneuvers, limits peak roll angle, behaves like an ordinary vehicle under normal driving, and can reduce roll during double lane‑change maneuvers.

Abstract

This paper uses Model Predictive Control theory to develop a framework for automobile stability control. The framework is then demonstrated with a roll mode controller which seeks to actively limit the peak roll angle of the vehicle while simultaneously tracking the driver’s yaw rate command. Initially, control law presented assumes knowledge of the complete input trajectory and acts as a benchmark for the best performance any controller could have on this system. This assumption is then relaxed by only assuming that the current driver steering command is available. Numerical simulations on a nonlinear vehicle model show that both control structures effectively track the driver intended yaw rate during extreme maneuvers while also limiting the peak roll angle. During ordinary driving, the controlled vehicle behaves identically to an ordinary vehicle. These preliminary results shows that for double lane change maneuvers, it is possible to limit roll angle while still closely tracking the driver’s intent.