基于RBF神经网络PID控制的列车ATO系统优化

针对在复杂环境下列车高速运行时,现有的Fuzzy-PID控制算法自适应性差在受到外界因素的干扰时会导致列车追踪误差较大的问题,提出了一种基于径向基(RBF)神经网络PID控制的列车速度控制算法。首先,在构建列车优化模型时,充分考虑列车经过电分相时必须处于惰行工况的特点,并且依据电分相和限速条件的特点将列车行驶过程中的区段进行了划分,简化了求解过程;然后使用RBF神经网络PID控制器对目标速度曲线进行追踪仿真,同时与现有的Fuzzy-PID控制器进行比较。实验结果表明,基于RBF神经网络PID控制算法能够实时有效的追踪目标速度曲线且追踪误差较小。

Optimization of train ATO system based on RBF neural network PID control

In order to solve the problem of poor adaptability of the existing fuzzy PID control algorithm when the train is running at high speed in complex environment, the tracking error of the train will be large when it is disturbed by external factors. This paper proposes a train speed control algorithm based on RBF neural network PID control. Firstly, when building the optimization model of the train, the characteristics that the train must be in the inert condition when passing through the phase separation area are fully considered. According to the characteristics of the electric phase separation and speed limit conditions, the sections in the process of the train running are divided to simplify the solving process. Then, the RBF neural network PID controller is used to track and simulate the target speed curve. At the same time, the existing fuzzy PID control comparison was made. The results show that the speed tracking algorithm based on RBF neural network is effective.