多工况适应的车辆质心侧偏角观测算法研究

Adaptive Estimation of Vehicle Sideslip Angle under Multi-status

研究汽车高速运行稳定性优化控制问题,在车辆稳定性控制中,质心侧偏角是衡量稳定性的重要指标,观测对于稳定性控制非常重要。针对目前车载多传感器信息的观测条件,为解决质心侧偏角观测的准确性、快速性和多工况适应性问题,提出了一种融合卡尔曼滤波和信号积分的质心侧偏角观测算法。观测算法充分考虑了车辆动力学特性,采用车辆运行过程的多种工况进行了算法设计及切换。最后在Matlab/Simulink平台上搭建了质心侧偏角观测仿真实验平台,通过多工况下的仿真,对所提出的质心侧偏角观测算法进行了仿真验证,结果表明能快速准确地矫正质心侧偏角,使稳态误差减小。

Since vehicle sideslip angle is an important parameter for vehicle stability control, its estimation is very important. In order to improve accuracy, rapidity and multi-status adaptability of vehicle sideslip angle estimation based on sensors on-board, an observation method coordinated kalman filter with lateral acceleration integration was proposed. This method developed calculation and switching algorithms under different conditions, which took vehicle dynamics into consideration. Based on Matlab/Simulink, a vehicle sideslip angle observation simulation platform was buih. This method was verified through simulation under different conditions, which showed rapid and accurate performances and the steady-state error is very small.