锻压技术

BP神经网络PID控制对液压控制系统的改进

英文标题：Improvement of hydraulic control system based on BP neural network PID control

作者：倪洪启王帅军王树强王建彬王凤双

分类号：TP273

出版年,卷(期):页码：2015,40(11):67-70

摘要：

闭式压力机的滑块在与工件接触过程中需要精确的位移控制，基于闭式压力机液压伺服系统的大惯性、时变性、高度非线性以及无法获得相对精确的数学模型等特点，单纯的PID控制器很难达到理想的控制效果。将BP网络与传统PID控制器结合，通过实时检测滑块在不同时刻的实际值、设定值和误差值，利用神经网络的在线自学习能力，在线调整PID参数KP、KI、KD，实现PID参数的最优组合,而且能够实现比较精确的滑块位移控制，控制性能明显优于传统PID控制器。通过仿真曲线对比分析可知，BP神经网络PID控制器对液压伺服控制系统的改进是非常有效的。

The slider of closed press needs precise control of displacement when contacted with the workpiece, based on the characteristics of the large inertia, time-varying, high nonlinera and differenty in obtaining relative precise mathematical model of hydraulic servo system for closed press, the ideal effect can not be achieved easily by traditional PID controller. Combined BP network with traditional PID controller, the optimal combination of parameters and more precise slider displacement control were realized by the real-time detection of sliders actual value, set value and error value at different time with the help of the on-line self-learning ability of neural network to adjust the PID parameters KP、KI、KD，online. The control performance of BP network is superior to traditional PID controller. The curve obtained by simulation comparison analysis shows that PID controller of BP neural network can effectively improve the hydraulic servo control system.

基金项目：

辽宁省教育厅科学技术研究项目（L2013170）

倪洪启（1967-），男，博士，副教授王帅军（1988-），男，硕士研究生

参考文献：

［1］赵升吨，闫伍超，王二郎，等.JA21-160型机械压力机的PLC控制[J].机床与液压,2000,28(6):15-19.Zhao S D,Yan W C,Wang E L, et al. JA21-160 type mechanical press PLC control [J].Machine Tool & Hydraulics,2000,28(6):15-19.
［2］Giulio D Emilia,Antonio Marra,Emanuela Natale.Use of neural networks for quick and accurate auto-tuning of PID controller[J].Robotics and Computer-Integrated Manufacturing,2007,(11):170-179.
［3］Psaltis D, Sideris A, Yamamura A A.A multilayer neural network controller[J].IEEE Control Systems Magazine,1988,(8):31-35.
［4］胡世军,张红香,张代录.PLC应用于温度控制系统的研究[J].锻压技术,2014,39(1):118-120.Hu S J,Zhang H X,Zhang D L. Research of PLC applied to temperature control system[J]. Forging & Stamping Technology, 2014,39(1):118-120.
［5］陈在平.可编程序控制器（PLC）系统设计[M].北京:电子工业出版社,2007.Chen Z P.The Programmable Logic Controller (PLC) System Design [M].Beijing: Electronic Industry Press, 2007.
［6］徐川川,朱凤凤,李铁.BP神经网络PID控制器在温室温度控制中的研究[J].中国农机化,2012,(2)：151-154.Tu C C,Zhu F F,Li T. Study on BP neural network PID controller in the temperature control of greenhouse[J]. China Agricultural Mechanization, 2012,(2)：151-154.
［7］陈怀忠.基于BP神经网络PID算法的水箱液位控制系统设计[J].实验技术与管理,2012,29(12):81-84.Chen H Z.The design of the water tank liquid level control based on BP neural network PID[J]. Experimental Technology and Management, 2012,29(12):81-84.
［8］朱海峰,杨志刚.基于BP神经网络整定的PID控制[J].国内外机电一体化技术,2006,(1)：55-58.Zhu H F,Yang Z G.The control of tuning PID based on BP neural network[J]. International Mechatronics Technology,2006,(1):55-58.
［9］易继锴，侯媛彬.智能控制技术[M].北京:北京工业大学出版社,2007.Yi J K,Hou Y B. Intelligent Control Technology[M].Beijing: Beijing Industrial University Press, 2007.
［10］张殿华，冯莹莹，骆宗安，等.BP神经网络PID控制对液压伺服控制的改进[J].钢铁研究学报,2009,21（3）:59-62.Zhang D H,Feng Y Y,Luo Z A,et al. Improvement of the hydraulic control system based on BP neural network PID control[J]. Journal of Iron and Steel Research, 2009,21（3）:59-62.
［11］刘金琨.先进PID控制及其MATLAB仿真[M].北京:电子工业出版社,2003.Lin J K.Advanced PID Control and MATLAB Simulation[M].Beijing: Electronic Industry Press, 2003.

服务与反馈：

【文章下载】【加入收藏】