Integrated chassis control for vehicle rollover prevention with neural network time-to-rollover warning metrics

Abstract

The rollover of road vehicles is one of the most serious problems related to transportation safety. In this article, a novel rollover prevention control system composed of rollover warning and integrated chassis control algorithm is proposed. First, a conventional time-to-rollover warning algorithm was presented based on the 3-degree of freedom vehicle model. In order to improve the precision of vehicle rollover prediction, a back-propagation neural network was adopted to regulate time to rollover online by considering multi-state parameters of the vehicle. Second, a rollover prevention algorithm based on integrated chassis control was investigated, where the active front steering and the active yaw moment control were coordinated by model predictive control methodology. Finally, the algorithms were evaluated under several typical maneuvers utilizing MATLAB/Simulink and Carsim co-simulation. The results show that the proposed neural network time-to-rollover metrics can be a good measure of the danger of rollover, and the roll stability of the simulated vehicle is improved significantly with reduced side slip angle and yaw rate by the proposed integrated chassis control rollover prevention system.