非结构道路环境下的智能汽车质心侧偏角估计

车辆质心侧偏角是描述车辆侧向运动状态的重要参量之一,其估计的精度直接影响车辆的安全控制,传统的质心侧偏角估计方法不能满足非结构道路环境下的智能汽车质心侧偏角估计的要求。通过建立3自由度智能汽车动力学模型,采用CarSim和MATLAB构建智能汽车整车参数化模型;基于扩展kalman滤波(EKF)算法,设计非结构道路环境下的状态观测器对智能汽车质心侧偏角进行估计。在高、低附着系数路面双移线工况和蛇形工况下,对状态观测器的估计效果进行联合仿真验证。仿真结果表明:该方法能较精确地估计出非结构道路环境下智能汽车的质心侧偏角。

Sideslip Angle Estimation of Intelligent Vehicle in Unstructured Road Environment

Sideslip angle of vehicle is one of the important parameters to describe the lateral motion state of vehicles, and the accuracy of its estimation directly affects the safety control of vehicles. The traditional sideslip angle estimation method can not meet the requirements of sideslip angle estimation of intelligent vehicles in unstructured road environment. Through the establishment of 3-dof vehicle dynamics model, CarSim and MATLAB were used to construct the parametric model of the whole intelligent vehicle. Based on the extended Kalman filter (EKF) algorithm, a state observer was designed to estimate sideslip angle of the intelligent vehicle. The estimation effect of the state observer was verified by the joint simulation under the double lane change test and double lane change test of road with high and low adhesion coefficient. The simulation results show that the proposed method can accurately estimate the sideslip angle of intelligent vehicle in unstructured road environment.