基于模糊RBF神经网络的轨道交通站台门预热控制技术研究

在严寒地区,恶劣的环境会对轨道交通站台门系统的使用产生不良影响,因此需采用预热控制系统保障设备的稳定运行。针对站台门预热控制系统难以使用精确的数学模型建模,系统控制参数设定困难,导致预热效果不稳定、安全性和可靠性降低的问题,文章将传统PID(比例-积分-微分)控制、模糊控制和RBF神经网络3种算法相融合,构建出一种智能、高效的控制算法;并通过仿真及试验证明该算法可以实现预热控制参数自动、快速迭代优化,具有较好的温度控制效果,可改良传统PID控制算法的动态控制性能及对非线性对象的控制稳定性,有一定的实际应用参考价值。

Research on preheating control technology of platform door of rail transit based on fuzzy RBF neural network

In the severe cold area,the severe environments have bad impacts on the use of the platform door system of rail transit,so it is necessary to adopt the preheating control system to ensure the stable operation of the equipment.In order to solve the problem that the platform door preheating control system has diffi culties to use the accurate mathematical model,and the system control parameters are difficult to set,which leads to the instability of the preheating effect,the reduction of the system security and reliability,this paper discusses the traditional PID(proportion,integral,differential)control,fuzzy control and RBF neural network to build an intelligent and effi cient control algorithm,and the simulation and experiment show that the algorithm realizes the automatic and fast iterative optimization of preheating control parameters,has better temperature control effect,improving the dynamic control performance of traditional PID control algorithm and the control stability of non-linear objects,and providing certain practical application reference value.