Intelligent Control Systems with LabVIEW™

227 Pages·2010·6.703 MB·English

by  Pedro Ponce-Cruz, Fernando D. Ramírez-Figueroa (auth.)

Intelligent Control Systems with LabVIEW™
Pedro Ponce-Cruz (cid:129) Fernando D. Ramírez-Figueroa
Intelligent Control Systems
with LabVIEW™
123
PedroPonce-Cruz,Dr.-Ing.
FernandoD.Ramírez-Figueroa,ResearchAssistanttoDoctorPonce
InstitutoTecnológicodeEstudiosSuperioresdeMonterrey
CampusCiudaddeMéxico
CalledelPuente222
Col.EjidosdeHuipulcoTlalpan
14380,México,D.F.
México
[email protected]
ISBN978-1-84882-683-0 e-ISBN978-1-84882-684-7
DOI10.1007/978-1-84882-684-7
SpringerLondonDordrechtHeidelbergNewYork
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Thepublisherandtheauthorsacceptnolegalresponsibilityforanydamagecausedbyimproperuseof
theinstructionsandprogramscontainedinthisbookandtheDVD.Althoughthesoftwarehasbeentested
withextremecare,errorsinthesoftwarecannotbeexcluded.
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Thisbookisdedicatedtomymotherandsonwithlove.
PedroPonce
To my parents Virginia and Fernando because this would not be possible without
theirunconditionalloveandsupport,andtomyadvisorandfriend,Pedroforgiving
me the opportunity to prove myself, and to all those who have accompanied me
alongthiswonderfuljourneyofknowledgeandcreation.
DavidRamirez
Preface
Controlsystemsarebecomingmoreimportanteveryday.Atthebeginning,thein-
dustryusedsequentialcontrolsforsolvingalotofindustrialapplicationsincontrol
systems,andthenthelinearsystemsgaveusahugeincreaseinapplyingautomatic
linearcontrolonindustrialapplication.Oneofthemostrecentmethodsforcontrol-
lingindustrialapplicationsisintelligentcontrol,whichisbasedonhumanbehavior
orconcerningnaturalprocess.
Nowadays,the topic of intelligentcontrolsystems has becomemore than a re-
searchsubjecttotheindustry.Thenumberofindustrialapplicationsisgrowingev-
eryday,fasterandfaster.Thus,newsoftwareandhardwareplatformsarerequired
inordertodesignanddevelopintelligentcontrolsystems.Thechallengeforthese
typesofsystemsistohaveanovelplatform,whichallowsdesigning,testingandim-
plementinganintelligentcontrollersysteminashortperiodoftime.Fortheindustry
andacademy,LabVIEW™isoneofthemostimportantsoftwareplatformsforde-
velopingengineeringapplicationsandcouldbeconnectedwithdifferenthardware
systems,aswellasrunningstandaloneprogramsforsimulatingthecontroller’sper-
formance(validatingthecontrollerbysimulationthenimplementingit).Inaddition,
LabVIEWisagraphicalprogramthatisveryeasytolearn.
Taking into account these advantages, the software platform described in this
bookisLabVIEWfromNationalInstruments™.Thebookisdividedinto7chapters
andgivesalltheinformationrequiredfordesigningandimplementinganintelligent
controller.
Chapter1providesanintroductiontobasicintelligentcontrolconceptsandcon-
cludesbyapplyingLabVIEWforimplementingcontrolsystems.Chapter2covers
in deep detail the fuzzy logic theoryand implementation.This chapter starts with
fundamentalfuzzylogictheoryforsupportingthemostimportantfuzzylogiccon-
trollersimplementedusingLabVIEW.
Chapter 3 deals with artificial neural networks. In this chapter a complete set
of tools for implementing artificial neural networks is presented. Basic examples
of neuralnetworks, such as perceptron,allow the students to understandthe most
importanttopologiesinartificialneuralnetworksformodelingandcontrollingsys-
tems. In Chap. 4 the reader can find neuro-fuzzy controllers, which combine the
vii
viii Preface
fuzzyinferencesystemswithanartificialneuralnetworktopology.Thus,theneuro-
fuzzy controllers are an interesting option for modeling and controlling industrial
applications. Chapter 5 discusses genetic algorithms,which are representationsof
the natural selection process. This chapter also examines how generic algorithms
can be used as optimization methods. Genetic programming is also explained in
detail.
Chapters 6 and 7 show different algorithms for optimizing and predicting that
could be combined with the conventional intelligent system methodologies pre-
sented in the previouschapters such as fuzzy logic, artificial neural networksand
neuro-fuzzysystems.The methodspresentedin Chaps.6 and7 are:simulatedan-
nealing,fuzzyclusteringmeans,partitioncoefficients,tabusearchandpredictors.
Supplemental materials supporting the book are available in the companion
DVD. The DVD includes all the LabVIEW programs (VIs) presented inside the
bookforintelligentcontrolsystems.
Thisbookwouldneverhavebeenpossiblewithoutthehelpofremarkablepeople
who believedin this project.I am notable to acknowledgeall of them here, but I
wouldliketothankEloisaAcha,GustavoValdes,JeannieFalcon,JavierGutierrez
andothersatNationalInstrumentsforhelpingustodevelopabetterbook.
Finally, I would like to thank the Instituto Tecnológico de Monterrey campus
CiudaddeMéxicoforsupportingthisresearchproject.Iwish torememberallmy
friendsandcolleagueswhogavemesupportduringthisresearchjourney.
ITESM-CCM Dr.PedroPonce-Cruz
MéxicoCity
Contents
1 IntelligentControlforLabVIEW ................................ 1
1.1 Introduction ............................................... 1
1.2 IntelligentControlinIndustrialApplications.................... 3
1.3 LabVIEW................................................. 4
References..................................................... 7
2 FuzzyLogic ................................................... 9
2.1 Introduction ............................................... 9
2.2 IndustrialApplications ...................................... 9
2.3 Background ............................................... 10
2.3.1 UncertaintyinInformation ............................ 11
2.3.2 ConceptofFuzziness................................. 11
2.4 FoundationsofFuzzySetTheory ............................. 11
2.4.1 FuzzySets.......................................... 12
2.4.2 BooleanOperationsandTerms......................... 14
2.4.3 FuzzyOperationsandTerms........................... 15
2.4.4 PropertiesofFuzzySets .............................. 18
2.4.5 Fuzzification ........................................ 18
2.4.6 ExtensionPrinciple .................................. 21
2.4.7 AlphaCuts ......................................... 23
2.4.8 TheResolutionPrinciple.............................. 24
2.4.9 FuzzinessofUncertainty.............................. 24
2.4.10 PossibilityandProbabilityTheories..................... 25
2.5 FuzzyLogicTheory ........................................ 26
2.5.1 FromClassicaltoFuzzyLogic ......................... 26
2.5.2 FuzzyLogicandApproximateReasoning................ 26
2.5.3 FuzzyRelations ..................................... 28
2.5.4 PropertiesofRelations................................ 28
2.5.5 Max–MinComposition ............................... 29
2.5.6 Max–StarComposition ............................... 30
2.5.7 Max–AverageComposition............................ 31
ix
x Contents
2.6 FuzzyLinguisticDescriptions ................................ 31
2.7 TheFuzzyLogicController.................................. 33
2.7.1 LinguisticVariables .................................. 33
2.7.2 MembershipFunctions ............................... 33
2.7.3 RulesEvaluation..................................... 33
2.7.4 MamdaniFuzzyController ............................ 34
2.7.5 Structure ........................................... 34
2.7.6 Fuzzification ........................................ 34
2.7.7 RulesEvaluation..................................... 35
2.7.8 Defuzzification ...................................... 35
2.7.9 TsukamotoFuzzyController........................... 35
2.7.10 Takagi–SugenoFuzzyController ....................... 36
2.7.11 Structure ........................................... 36
2.7.12 Fuzzification ........................................ 36
2.7.13 RulesEvaluation..................................... 36
2.7.14 CrispOutputs ....................................... 37
2.8 ImplementationoftheFuzzyLogicControllers
UsingtheIntelligentControlToolkitforLabVIEW .............. 37
2.8.1 Fuzzification ........................................ 38
2.8.2 RulesEvaluation..................................... 40
2.8.3 Defuzzification:CrispOutputs ......................... 41
2.9 ClassicalControlExample................................... 43
References..................................................... 46
FutherReading ................................................. 46
3 ArtificialNeuralNetworks ...................................... 47
3.1 Introduction ............................................... 47
3.2 ArtificialNeuralNetworkClassification........................ 55
3.3 ArtificialNeuralNetworks................................... 56
3.3.1 Perceptron.......................................... 57
3.3.2 Multi-layerNeuralNetwork ........................... 60
3.3.3 TrigonometricNeuralNetworks........................ 71
3.3.4 KohonenMaps ...................................... 79
3.3.5 BayesianorBeliefNetworks........................... 84
References..................................................... 87
FutherReading ................................................. 88
4 Neuro-fuzzyControllerTheoryandApplication ................... 89
4.1 Introduction ............................................... 89
4.2 TheNeuro-fuzzyController.................................. 90
4.2.1 TrigonometricArtificialNeuralNetworks................ 91
4.2.2 FuzzyClusterMeans ................................. 96
4.2.3 PredictiveMethod ................................... 98
4.2.4 ResultsUsingtheController...........................100
4.2.5 ControllerEnhancements .............................101
Contents xi
4.3 ANFIS:AdaptiveNeuro-fuzzyInferenceSystems ...............106
4.3.1 ANFISTopology ....................................108
References.....................................................122
FutherReading .................................................122
5 GeneticAlgorithmsandGeneticProgramming ....................123
5.1 Introduction ...............................................123
5.1.1 EvolutionaryComputation ............................123
5.2 IndustrialApplications ......................................124
5.3 BiologicalTerminology .....................................125
5.3.1 SearchSpacesandFitness.............................125
5.3.2 EncodingandDecoding...............................125
5.4 GeneticAlgorithmStages....................................126
5.4.1 Initialization ........................................127
5.4.2 Selection ...........................................128
5.4.3 Crossover...........................................129
5.4.4 Mutation ...........................................130
5.5 GeneticAlgorithmsandTraditionalSearchMethods .............134
5.6 ApplicationsofGeneticAlgorithms ...........................135
5.7 ProsandConsofGeneticAlgorithms..........................136
5.8 SelectingGeneticAlgorithmMethods .........................136
5.9 MessyGeneticAlgorithm....................................137
5.10 OptimizationofFuzzySystemsUsingGeneticAlgorithms........138
5.10.1 CodingWholeFuzzyPartitions ........................138
5.10.2 StandardFitnessFunctions ............................139
5.10.3 CodingRuleBases...................................139
5.11 AnApplicationoftheICTLfortheOptimization
ofaNavigationSystemforMobileRobots .....................140
5.12 GeneticProgrammingBackground............................143
5.12.1 GeneticProgrammingDefinition .......................143
5.12.2 HistoricalBackground................................144
5.13 IndustrialApplications ......................................144
5.14 AdvantagesofEvolutionaryAlgorithms........................144
5.15 GeneticProgrammingAlgorithm .............................145
5.15.1 Length .............................................146
5.16 GeneticProgrammingStages.................................146
5.16.1 Initialization ........................................146
5.16.2 Fitness .............................................147
5.16.3 Selection ...........................................147
5.16.4 Crossover...........................................147
5.16.5 Mutation ...........................................148
5.17 VariationsofGeneticProgramming ...........................149
5.18 GeneticProgramminginDataModeling .......................150
5.19 GeneticProgrammingUsingtheICTL.........................150
xii Contents
References.....................................................153
FutherReading .................................................154
6 SimulatedAnnealing,FCM,PartitionCoefficientsandTabuSearch .155
6.1 Introduction ...............................................155
6.1.1 IntroductiontoSimulatedAnnealing....................156
6.1.2 PatternRecognition ..................................157
6.1.3 IntroductiontoTabuSearch ...........................157
6.1.4 IndustrialApplicationsofSimulatedAnnealing...........158
6.1.5 IndustrialApplicationsofFuzzyClustering ..............158
6.1.6 IndustrialApplicationsofTabuSearch ..................158
6.2 SimulatedAnnealing........................................159
6.2.1 SimulatedAnnealingAlgorithm........................161
6.2.2 SampleIterationExample.............................163
6.2.3 ExampleofSimulatedAnnealing
UsingtheIntelligentControlToolkitforLabVIEW........163
6.3 FuzzyClusteringMeans.....................................166
6.4 FCMExample .............................................170
6.5 PartitionCoefficients........................................172
6.6 ReactiveTabuSearch .......................................173
6.6.1 IntroductiontoReactiveTabuSearch....................173
6.6.2 Memory............................................174
References.....................................................189
FutherReading .................................................190
7 Predictors.....................................................191
7.1 IntroductiontoForecasting ..................................191
7.2 IndustrialApplications ......................................192
7.3 ForecastingMethods........................................193
7.3.1 QualitativeMethods..................................193
7.3.2 QuantitativeMethods.................................194
7.4 RegressionAnalysis ........................................194
7.5 ExponentialSmoothing .....................................194
7.5.1 Simple-exponentialSmoothing.........................195
7.5.2 Simple-exponentialSmoothingAlgorithm ...............195
7.5.3 Double-exponentialSmoothing ........................196
7.5.4 Holt–WinterMethod .................................197
7.5.5 Non-seasonalBox–JenkinsModels .....................198
7.5.6 GeneralBox–JenkinsModel...........................199
7.6 MinimumVarianceEstimationandControl.....................200
7.7 ExampleofPredictorsUsingtheIntelligentControlToolkit
forLabVIEW(ICTL) .......................................202
7.7.1 ExponentialSmoothing ...............................202
7.7.2 Box–JenkinsMethod .................................203
7.7.3 MinimumVariance...................................204