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Fuzzy neural network control method with compensation
for time-delay system |
| PAN Hai-peng, LV Yong-song |
| Institute of Automation, Zhejiang SciTech University, Hangzhou 310018, China |
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Abstract The traditional fuzzy neural network (FNN) controller has the disadvantage of long setting time for timedelay system. A new method was proposed to obtain the compensation value aimed at the problem. The parameters for timedelay model were identified online based on the recursive least square (RLS) algorithm. Then the output of the system can be predicted, and the compensation value was obtained. An FNN controller of twodimension input with compensation was designed. Simulation results show that the method has the characteristic of short setting time, high control precision and obvious improvement compared with the traditional FNN controller.
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Published: 01 July 2010
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时滞系统的模糊神经网络补偿控制
针对传统的模糊神经网络（FNN）在线控制方法用于控制时滞对象时存在调节时间较长的问题,分析产生这一现象的原因,对传统的模糊神经网络在线控制进行改进,给出一种新的确定补偿量的方法.基于递推最小二乘(RLS)法在线辨识对象模型,通过时滞对象模型预测对象输出的变化,利用补偿方法得到控制量的补偿量.设计二维输入的带补偿的模糊神经网络控制器,进行实验与仿真研究.仿真结果表明,该补偿方法调节时间短,控制精度高,比传统的模糊神经网络的控制效果明显.
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