内模解耦单神经元自适应PID仿真研究  

Internal Model Decoupling Based on Single-neuron Adaptive PID

作　　者：姚培[1] 郑恩让[1] 

机构地区：[1]陕西科技大学,电气与信息工程学院,西安710021

出　　处：《计算机仿真》2008年第7期155-157,共3页Computer Simulation

摘　　要：为了解决多变量大时滞系统的耦合问题,根据内模控制原理和单神经元的在线自学习能力,提出一种基于模型的单神经元自适应PID内模解耦算法,并详细分析了其在多变量大时滞系统中的解耦原理。内模解耦是运用解耦预估补偿器将一个多输入多输出的系统补偿为多个单输入单输出的系统、并将对象模型进行对角优势化,补偿后的主对角元素即可作为内模控制的预估模型。仿真结果表明,这种新的内模解耦算法具有相当好的解耦能力、较好的快速性和抗干扰能力。In order to solve the coupling problem in large delay system with more variables,a new decoupling algorithm with single neuron self–adaptive PID internal model is recommended,which is based on internal model control theory and the on-line self-learning ability of single neuron.Furthermore,the decoupling theory in large delay system with more variables is analyzed.Internal decoupling can tranfer a more in and more out system into a single in and single out system with more sub-systems by using decoupling predictive compensator,and turn the object model into diagonal predominance.After doing all the work,the principal diagonal elements can be regarded as the predictive model of internal control.Experimental results show that the decoupling capability of this new internal model algorithm is rather good.It also shows fast property and better anti-disturbance capability.

关 键 词：解耦控制 单神经元 自适应控制器 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]