基于BP神经网络的中低速磁浮列车速度估计算法

On Estimation Algorithm for Low Speed Maglev Train Based on BP Neural Network

提出结合磁浮列车牵引用直线感应电机特点,利用BP神经网络算法,实现对磁浮列车的运行速度观测。通过对一直线感应电机动态模拟实验台进行研究表明,理论估算和实测速度相吻合,初步表明该方法是可行的。该方法的实现,节省了现有磁浮列车速度检测系统昂贵的成本,减少了系统硬件设备的复杂性,提高了整个磁浮列车系统工作的可靠性。

Maglev train is a new vehicle without support wheel and its movement speed is gained by means of a special measure equipment. By using BP neural network arithmetic, the maglev train speed can be estimated by means of combining characteristics of its traction linear induction motor. Based on a dynamic simulation experiment, real speed measure result is near to theory calculation. This shows the method is feasible. In view of the method, it can economize expensive cost for speed measurement of maglev train, reduce system equipment complexity, and also advance work reliability of entire train system.