基于滑模控制的卡尔曼滤波在列车定位中的研究

为了提高城市轨道交通中轮轴速度传感器与加速度计组合定位的精度,提出一种基于滑模控制的改进卡尔曼滤波算法。对于组合定位来说,由于卡尔曼滤波不能很好地修正加减速过程中的空转打滑误差,考虑到滑模控制器的滑动模态与系统的参数及扰动无关,提出采用基于滑模控制的改进卡尔曼滤波来进一步降低误差。其基本思路是应用指数趋近律滑模变结构来改善里程计算值,然后再进行卡尔曼滤波。并利用仿真软件对上述过程进行验证,仿真结果表明:基于滑模控制的改进卡尔曼滤波算法,能够在一定程度上减小空转打滑误差,进一步提高定位的精度。最后通过与其他卡尔曼滤波改进算法对比,得出基于滑模控制的卡尔曼滤波方法结构更为简单,也能保证一定的精度。

Research on Train Positioning Based on Kalman Filtering of Sliding Mode Control

In order to improve the accuracy of the combined positioning with wheel speed sensor and accelerometer in urban rail transit, a Kalman filtering algorithm based on sliding mode control is proposed. Since Kalman filtering can not well correct the error caused by idling or skidding in the acceleration and deceleration processes in the integrated positioning, the improved Kalman filtering based on sliding mode control is used to further reduce the error in view of the fact that the sliding mode of the sliding mode controller is independent of the parameters and disturbances of the system. The basic idea is to apply the exponential approaching law sliding mode variable structure to improve the mileage calculation value, and then proceed with Kalman filtering. The process above is verified by simulation software. The simulation results show that the Kalman filtering algorithm based on sliding mode control can reduce the error caused by idling or skidding to some extent and further improve the accuracy of positioning as well. Finally, by comparing with other improved Kalman filtering algorithms, this Kalman filtering method based on sliding mode control is simpler and effective in maintaining necessary precision.