低附着路况条件下车辆横向稳定性控制

Vehicle Lateral Stability Control under Low Adhesion Road Conditions

针对在冰雪环境下车辆横向稳定控制,为解决在低附着、分布不均的路面情况下车辆对参考轨迹的稳定跟踪问题,设计了基于神经网络调节的模糊PID(Proportional-Integral-Differential)制器,以及基于线性化车辆模型的模型预测控制(MPC:Model Predictive Controll)。以路面附着系数及车辆速度作为输入构建BP(Back-Propagation)神经网络,输出调节系数优化模糊PID控制器控制性能;设计了十自由度模型表征车辆在冰雪环境下的动力学特性,使用MPC实现车辆横向稳定控制。使用CarSim/Simulink进行联合仿真实验,结果表明该控制器能显著提高车辆轨迹跟踪性能。

Aiming at the characteristic that the vehicle is more prone to instability in the snow and ice environment,the stable tracking problem of the vehicle to the reference trajectory under the low adhesion and uneven distribution condition of the road surface is studied.To address this,a fuzzy PID(Proportional-Integral-Differential)controller model based on neural network regulation and MPC(Model Predictive Control)a linearized vehicle model are designed.The controller takes the road adhesion coefficient and vehicle speed as input to construct a BP(Back-Propagation)neural network and outputs the adjustment coefficient to optimize the control performance of the PID controller.A ten-degree-of-freedom model is designed to characterize the dynamic characteristics of the vehicle in snow and ice-covered environments,and the lateral stability control of the vehicle is realized by using MPC.CarSim/Simulink is used for co-simulation experiments.Results show that the controller can significantly improve the performance of vehicle trajectory tracking.The dynamic characteristics of the vehicle under snow and ice are analyzed,and good simulation results are obtained.