基于高速动车组重联网络控制系统时延的研究

文章首先建立高速动车组重联网络控制系统模型并分析前向通道与反向通道时延,基于BP神经网络递推预测的方法对网络控制系统未来的输出进行预测。然后提出一种快速隐式广义预测控制算法(IGPC)对预测的时延进行补偿,IGPC算法的原理是根据系统输入与输出数据,并利用广义预测控制(Generalized Predictive Control,GPC)算法与动态矩阵控制律(DMC)的等价性,直接求解最优控制律。IGPC算法比GPC算法的计算量更小且效率更高,既能节省时间成本又能保证高速动车组网络控制的实时性。最后将BP神经网络递推预测的方法与IGPC、GPC结合起来,分别采用无时延补偿基于BP神经网络预测的GPC算法、有时延补偿基于BP神经网络预测的GPC算法及有时延补偿基于BP神经网络预测的IGPC算法进行实验仿真,实验结果表明:相比较于其它两种算法,有时延补偿基于BP神经网络预测的IGPC算法可较好地跟踪标准参考方波,在初始阶段的震荡时间最短且超调量也最小。故有时延补偿基于BP神经网络预测的IGPC算法为最优算法。

In this paper, the reconnection network control system model of high-speed emu is firstly established and the time delay of forward channel and reverse channel is analyzed. The future output of the network control system is predicted based on the method of BP neural network recursion prediction. Then, a fast implicit generalized predictive control(IGPC) algorithm is proposed to compensate the time delay of prediction. The principle of IGPC algorithm is based on the input and output data of the system, and the equivalence between generalized predictive control(GPC) algorithm and dynamic matrix control(DMC) law is used to directly solve the optimal control law. Compared with GPC algorithm, IGPC algorithm has less computation and higher efficiency, which can not only save the time cost but also guarantee the real-time performance of high-speed EMU network control. Finally, the recursive prediction method of BP neural network is combined with IGPC and GPC, and the experimental simulations are carried out by using GPC algorithm without delay compensation based on BP neural network prediction, GPC algorithm based on BP neural network prediction and IGPC algorithm based on BP neural network prediction. The experimental results show that compared with other two algorithms, a time delay compensation based on BP neural network prediction algorithm of IGPC can better tracking reference standard square wave, in the initial stage of the shortest time and overshoot volume is the smallest. Therefore, the IGPC algorithm based on BP neural network is the optimal one.