Research on vehicle critical condition prediction and optimal yaw moment intervention based on nonlinear dynamics and Monte Carlo simulation

Abstract

Timely and effective yaw moment intervention is required to suppress the instability tendency of vehicle under critical conditions, which is mainly caused by the overshoot of state parameters. As the development of vehicle nonlinear dynamics, the prediction of vehicle critical condition enables the vehicle to stabilize in this condition by timely yaw moment intervention. The yaw moment intervention law based on the prediction of critical conditions cannot be solely investigated by the initial motion states, since the online iterative optimization is an indispensable part in the predictive control. In this paper, the combination of threshold model, quadratic optimal yaw moment prediction method and Monte Carlo simulation provides a new approach to investigate the optimal open loop yaw moment intervention law based on initial motion states, which can be used to maintain the stability of vehicle plane motion in critical situations and optimize the predictive control method. Specifically, a total of 480,000 samples of quadratic yaw moment intervention parameters and corresponding system responses are obtained and analysed. It is certified that the optimized quadratic yaw moment can effectively suppress the overshoot of system responses. Specifically, statistics indicate that the initial value of yaw moment intervention plays an important role in preventing instability, but with the increase of speed and steering angle, the final yaw moment will be the decisive factor to maintain the lateral stability.