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浙江大学学报(工学版)
J4  2012, Vol. 46 Issue (2): 194-198    DOI: 10.3785/j.issn.1008-973X.2012.02.002
    
Adaptive networked control system based on delay prediction
using neural network
YU Xiao-ming, JIANG Jing-ping
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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Abstract  

Aiming at existent randomness, time varying and uncertainty of the information transmission time delay in the networked control systems, the timestamped linear neural network (TSLNN) is adopted to predict the time delay in real-time, on line. Using measurement time delay values of the previous sampled period in actual network as the input data set for the neural network, the widrow-hoff learning rule is chosen as the training algorithm of neural network; With predicted network delay value, and using one-order Pade method to linearize the delay element, the linear mathematical model of the brushless direct-current motor drive networked control systems is established; The model reference adaptive control (MRAC) strategy is introduced to design the close-loop controller for the control system; The results of digital simulation prove that based on TSLNN time delay prediction in realtime, the MRAC for the brushless direct-current motor drive networked control system is feasible, and the dynamic and static response performances of the system are satisfied.

Published: 20 March 2012
CLC:  TP 273  
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Cite this article:

YU Xiao-ming, JIANG Jing-ping. Adaptive networked control system based on delay prediction
using neural network. J4, 2012, 46(2): 194-198.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2012.02.002     OR     http://www.zjujournals.com/eng/Y2012/V46/I2/194


基于神经网络延时预测的自适应网络控制系统

针对网络控制系统存在着随机、时变、不确定的信息传输延时,采用带有时间戳的线性神经网络（TSLNN)进行在线延时预测,实时地获得当前采样周期的网络传输延时预测值.该方法选取3个先验的网络实测延时值作为神经网络的输入样本,选用widrow-hoff学习规则作为神经网络的训练算法；应用网络传输延时预测值,并采用一阶Pade方法,对数学模型中的延时环节进行线性化处理,从而获得无刷直流电机调速网络控制系统的线性数学模型；最后,利用模型参考自适应控制方法（MRAC）设计闭环控制器.仿真结果表明,将基于TSLNN在线延时预测的MRAC方法应用于无刷直流电机调速网络控制系统中,可以获得令人满意的系统动、静态性能.

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