[1]韩桂华,王鹏飞,张此军.电液伺服系统神经网络辨识及控制器设计[J].哈尔滨理工大学学报,2017,(05):18-23.[doi:10. 15938/j. jhust. 2017. 05. 004]
 HAN Gui-hua,WANG Peng-fei,ZHANG Ci-jun.Neural Network Identification and Controller on Electro-hydraulic Servo System[J].哈尔滨理工大学学报,2017,(05):18-23.[doi:10. 15938/j. jhust. 2017. 05. 004]
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电液伺服系统神经网络辨识及控制器设计()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2017年05期
页码:
18-23
栏目:
材料科学与工程
出版日期:
2017-10-25

文章信息/Info

Title:
Neural Network Identification and Controller on Electro-hydraulic Servo System
文章编号:
1007-2683(2017)05-0018-06
作者:
韩桂华 王鹏飞 张此军
哈尔滨理工大学 机械动力工程学院,黑龙江 哈尔滨 150080
Author(s):
HAN Gui-hua WANG Peng-fei ZHANG Ci-jun
School of Mechanical and Power Engineering,Harbin University of Science and Technology ,Harbin 150080,China
关键词:
:改进 BP 神经网络系统辨识智能权函数模糊控制电液位置伺服系统遗传算法
Keywords:
:improved BP neural network system identification intelligent weight function fuzzy control electro-hydraulic position servo system genetic algorithm
分类号:
TP183,TP273
DOI:
10. 15938/j. jhust. 2017. 05. 004
文献标志码:
A
摘要:
针对阀控缸电液位置伺服系统非线性建模问题,采用神经网络进行系统模型辨识。采用 LM 遗传算法对三层 BP 神经网络的权值和阈值进行修正,通过训练系统的输入/输出数据建立非线性系统辨识模型。基于此模型,设计模糊 PI 控制器,利用智能权函数在线自动调整和修改模糊控制器的规则。利用 xPC 技术建立阀控缸伺服实验台,以实验台阶跃输出信号作为改进 BP 神经网络辨识信号,以实验台正弦输出信号作为验证信号。实验表明:该神经网络辨识模型的可信性 得以验证;通过对比智能权函数模糊 PI 控制器和模糊控制器的实验曲线,表明前者控制效果更好。
Abstract:
The neural networks system identification was used in nonlinear model on valve-control-cylinder electro-hydraulic position servo system. The three layers BP neural network weights and threshold were optimized using LM genetic algorithm,the relationship of system input and output was analyzed and neural network identification model was presented. A kind of fuzzy PI controller was designed based on the model,which can automatically adjust and modify the rules of fuzzy controller by using the intelligent weight function. A real -time electro-hydraulic servo test bench was built with the xPC technique. The test bench step output was used to identify in the improved BP neural network and the sinusoidal output was used to verify in experiment. Experiment results show that the credibility is verified on neural network identification model; and that the control effect of the intelligent weight function fuzzy PI controller is better than the fuzzy controller.

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[1]吴雪雨.油藏系统函数型连接神经网络辨识方法研究[J].哈尔滨理工大学学报,2009,(03):29.
 WU Xue-yu.Identification Method Research of Functional Link Nets for Oil Reservoir Systems[J].哈尔滨理工大学学报,2009,(05):29.

更新日期/Last Update: 2017-11-15