模糊神经网络PID控制在磨粉过程中的应用  

Application of Fuzzy Neural Network PID Control in Grinding Process

作　　者：穆海芳[1,2] 韩君 何康[1,2] 李明[1] MU Hai-fang;HAN Jun;HE Kang;LI Ming(Suzhou University,Suzhou 234000,China;Suzhou Machinery and Equipment Collaborative Innovation Engineering Technology Research Center,Suzhou 234000,China)

机构地区：[1]宿州学院,安徽宿州234000 [2]宿州市机械装备协同创新工程技术研究中心,安徽宿州2340000

出　　处：《廊坊师范学院学报（自然科学版）》2019年第3期32-35,41,共5页Journal of Langfang Normal University(Natural Science Edition)

基　　金：2018年安徽省科技计划项目“基于多源信号融合的外骨骼下肢康复机器人研制”(18030901023);2017年安徽省自然科学基金项目“分布式传感数据因果发现的制造系统质量监控研究”(1708085ME104)

摘　　要：磨煤机制粉系统具有非线性、时滞性大等特点,而且其输入量和输出量之间高度耦合,采用常规的控制方法难以实现良好的效果。为此提出模糊控制与神经网络结合的结构,采用误差反传优化网络权值,用于磨煤机制粉系统的控制。首先采用模糊算法对神经网络的输入值进行预处理,然后采用误差反传算法调整网络的权值,实现了PID控制器参数的自适应调整。仿真实验表明,该方法能解决耦合性、时滞性问题,超调量小,跟踪效果好,具有良好的鲁棒性和适应性。The pulverizing system of coal mill has the characteristics of nonlinearity and large time delay,and its input and output are highly coupled.It is difficult to achieve good results by using conventional control methods.Therefore,this paper proposes a structure combining fuzzy control and neural network,the weights of network was optimized by using the error back propagation,which is used for the control of pulverizing system of coal mill.Firstly,fuzzy algorithm is used to preprocess the input value of neural network,then the back propagation algorithm is used to adjust the weight of the network,adaptive adjustment of PID controller parameters is realized.Simulation results show that this method can solve coupling and time delay problems,which has small overshoot,good tracking effect and good robustness and adaptability.

关 键 词：磨煤机 神经网络PID 模糊算法 

分 类 号：TD453[矿业工程—矿山机电]