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202Vol.20No.220016COMPUTINGTECHNOLOGYANDAUTOMATIONJun2001:10036199(2001)02001005:20000813:(1972),,,CAD;(1968),,,,Matlab1,2(1.,362011;2.,300072):,MatlabSimulink,,,,:;;;:TP273:ASimulationandParametricEvolutionaryComputationofDoubleLoopDCMotorControlSystemBasedonMatlabZhouKaiting1ZhengLixin1,2(1.CollegeofPrecisionInstruments,TianjinUnv.,Tianjin3000722.CollgeofInformationScienceandEngineering,HuaqiaoUnv.,Quanzhou362011)Abstract:ThispaperputforwardanewmethodofdesigningDoubleLoopDCMotorControlSystem(DLM).Firstly,undertheenvironmentofMatlabandSimulink,wecorrectlysetupthesimulationdiagramoftheDLM.Then,weestablishtheoptimizationmodelwithtypicalperformancecriteriaasobjectfunctionandtheparameternumericalrangesastheconstraints.Finally,ainnovativegeneticalgorithmispresentedtosolvetheoptimizationrangesastheconstraints.Finally,ainnovativegeneticalgorithmispresentedtosolvetheoptimizationproblem.Theyieldedsolutionsgreatlyimprovetheperformanceofthesystem.Keywords:DoubleLoopControlSystem,Simulation,GeneticAlgorithms,Optimizationdesign1 引言(DLM)[1],(EDM),DLM,,EDM,,,,(GA)[3,4]GA,,,[5,6]GADLM,202:Matlab112 双闭环系统的Simulink仿真1[2]Kn,Tn,Ki,Ti,Id,IdL,n(s)图1 典型双闭环系统控制论框图2.1SimulinkDLM,ASR,Simulink,PIPI,2,ASR(PI)SwitchSwitchlASR图2 与实际ASR特性等效的PI调节器Simulink框图2.2imulink1,IdIdL,,IdIdL,,IdIdL,,1Simulink3Switch40.1,Sat301220016图3 电机的Simuling仿真框图2.3Simuling,4,G(S),D(s)(),MatlabSimuling;SimulingMFn1Fn2,,ITAEISTE,J=ITAE=0tûe(t)ûdt(Fn1:y=x,Fn2:y=abs(x))J=ISTE=0t2e2(t)dt(Fn1:y=x*x,Fn2:y=x*x)图4 Simuling环境下控制系统性能指标J求取框图3 基于遗传算法的系统优化设计DLMASRACR4Kn,Tn,Ki,Ti,3.1GA,(SAGA)SAGA:,;,,SAGAsPcPm:,;,,,PcPmDLM,SAGA,:M,N,202:Matlab13:X(M,N)=x11x12x1nx21x22x2nxM1xM2xMn=x1x2xMn:dn=2M,Mi,j,ij(xin-xjn)2M(M-1)(xnmax-xnmin)2(1):PC(n)=min{0.6,KCdn},Pm(n)=min{0.2,Km(1-dn)}(2)xnmaxxnminn,KcKm,SAGA,,(,)3.21Idm,Usm1()TonToiTsTlTmKsRCeABUsmIdm0.010.00150.00170.0150.3140.010.550.1680.010.75210.013.3Kn*=7.1,Tn*=0.164,Ki*=0.8170,Ti*=0.0149,SAGA14,DLMSimuling,DLM:minJ=ISTE=0t2e2(t)dt;0Id1.043Idm(1-A)Kn*Kn(2+A)Kn*(1-A)Tn*Tn(2+A)Tn*(1-A)Ki*Ki(2+A)Ki*(1-A)Ti*Ti(2+A)Ti*,A[0,1],DLMSAGA:(1)Step1:MKcKmMAXGEN(2)Step2:gen=1X,X=[Kn,Tn,Ki,Ti]M,X(M,4)(),(3)Step3:X(M,4),(1)(2),(4)Step4:,,([7])(1420016),,elitist:(ASRACR)Simuling,J,Eval(XK)=J;(K=1,2,,M)Eval(XK)M,v*maxevalStep5genMAXGEN,Step3;genMAXGEN,v*maxeval,A=0.5,Kc=1.5,Km=1,POPSIZE=20,MAXGEN=100,(*)22GAEDM()KnTnKiTiR%tsR%*t*sIdL=4.019.80.1321.140.0214.60.528.20.73IdL=5.519.80.051.070.0193.80.626.90.81IdL=7.019.90.041.130.0203.00.775.60.93(IdL5.5A),SAGA,,,5,SAGA图5 EDM和GA对应的系统阶跃电流和速度响应4 结论,,SAGA,,,,,SAGA[1],.[M].,1986,12.[2].()[M].,1995,275286,136137.[3]JHHyun,COLee.Optimizationoffeedbackgainsforahydraulicservosystembygeneticalgorithms[J].ProcInstnMechEngrs,1998,212,395-401.