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A First Course in FUZZY and NEURAL CONTROL

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  • 1 A PRELUDE TO CONTROL THEORY

    • 1.1 Anancientcontrolsystem......................

    • 1.2 Examplesofcontrolproblems....................

      • 1.2.1 Open-loopcontrolsystems..................

      • 1.2.2 Closed-loopcontrolsystems.................



    • 1.3 Stableandunstablesystems.....................

    • 1.4 Alookatcontrollerdesign......................

    • 1.5 Exercisesandprojects



  • 2 MATHEMATICAL MODELS IN CONTROL

    • 2.1 Introductoryexamples:pendulumproblems............

      • 2.1.1 Example:fixed pendulum

      • 2.1.2 Example:invertedpendulumonacart...........



    • 2.2 Statevariablesandlinearsystems..................

    • 2.3 Controllability and observability

    • 2.4 Stability................................

      • 2.4.1 Dampingandsystemresponse

      • 2.4.2 Stability of linear systems..................

      • 2.4.3 Stability of nonlinear systems................

      • 2.4.4 Robuststability



    • 2.5 Controllerdesign...........................

    • 2.6 State-variablefeedbackcontrol

      • 2.6.1 Second-ordersystems

      • 2.6.2 Higher-ordersystems.....................



    • 2.7 Proportional-integral-derivativecontrol...............

      • 2.7.1 Example:automobilecruisecontrolsystem

      • 2.7.2 Example:temperaturecontrol

      • 2.7.3 Example: controlling dynamics of a servomotor



    • 2.8 Nonlinearcontrolsystems

    • 2.9 Linearization

    • 2.10Exercisesandprojects



  • 3FUZZYLOGICFORCONTROL iv CONTENTS

    • 3.1 Fuzzinessandlinguisticrules

    • 3.2 Fuzzysetsincontrol

    • 3.3 Combiningfuzzysets.........................

      • 3.3.1 Minimum,maximum,andcomplement...........

      • 3.3.2 Triangularnorms,conorms,andnegations.........

      • 3.3.3 Averagingoperators .....................



    • 3.4 Sensitivityoffunctions........................

      • 3.4.1 Extrememeasureofsensitivity ...............

      • 3.4.2 Averagesensitivity......................



    • 3.5 Combiningfuzzyrules ........................

      • 3.5.1 Productsoffuzzysets ....................

      • 3.5.2 Mamdanimodel .......................

      • 3.5.3 Larsenmodel.........................

      • 3.5.4 Takagi-Sugeno-Kang(TSK)model .............

      • 3.5.5 Tsukamotomodel ......................



    • 3.6 Truthtablesforfuzzylogic .....................

    • 3.7 Fuzzypartitions ...........................

    • 3.8 Fuzzyrelations ............................

      • 3.8.1 Equivalencerelations.....................

      • 3.8.2 Orderrelations........................



    • 3.9 Defuzzification ............................

      • 3.9.1 Centerofareamethod....................

      • 3.9.2 Height-centerofareamethod ................

      • 3.9.3 Maxcriterionmethod ....................

      • 3.9.4 Firstofmaximamethod...................

      • 3.9.5 Middleofmaximamethod..................



    • 3.10Levelcurvesandalpha-cuts.....................

      • 3.10.1Extensionprinciple......................

      • 3.10.2Imagesofalpha-levelsets ..................



    • 3.11Universalapproximation.......................

    • 3.12Exercisesandprojects ........................



  • 4FUZZYCONTROL

    • 4.1 Afuzzycontrollerforaninvertedpendulum............

    • 4.2 Mainapproachestofuzzycontrol..................

      • 4.2.1 MamdaniandLarsenmethods ...............

      • 4.2.2 Model-basedfuzzycontrol..................



    • 4.3 Stability of fuzzy control systems..................

    • 4.4 Fuzzycontrollerdesign........................

      • 4.4.1 Example:automobilecruisecontrol ............

      • 4.4.2 Example: controlling dynamics of a servomotor



    • 4.5 Exercisesandprojects ........................



  • 5 NEURAL NETWORKS FOR CONTROL CONTENTS v

    • 5.1 Whatisaneuralnetwork?......................

    • 5.2 Implementingneuralnetworks....................

    • 5.3 Learning capability..........................

    • 5.4 Thedeltarule.............................

    • 5.5 The backpropagation algorithm...................

    • 5.6 Example1:traininganeuralnetwork ...............

    • 5.7 Example2:traininganeuralnetwork ...............

    • 5.8 Practicalissuesintraining......................

    • 5.9 Exercisesandprojects ........................



  • 6NEURALCONTROL

    • 6.1 Whyneuralnetworksincontrol...................

    • 6.2 Inversedynamics...........................

    • 6.3 Neuralnetworksindirectneuralcontrol ..............

    • 6.4 Example:temperaturecontrol....................

      • 6.4.1 Aneuralnetworkfortemperaturecontrol .........

      • 6.4.2 SimulatingPIcontrolwithaneuralnetwork........



    • 6.5 Neuralnetworksinindirectneuralcontrol.............

      • 6.5.1 System identification.....................

      • 6.5.2 Example: system identification ...............

      • 6.5.3 Instantaneouslinearization .................



    • 6.6 Exercisesandprojects ........................



  • 7 FUZZY-NEURAL AND NEURAL-FUZZY CONTROL

    • 7.1 Fuzzyconceptsinneuralnetworks .................

    • 7.2 Basicprinciplesoffuzzy-neuralsystems ..............

    • 7.3 Basicprinciplesofneural-fuzzysystems ..............

      • 7.3.1 Adaptivenetworkfuzzyinferencesystems.........

      • 7.3.2 ANFISlearningalgorithm..................



    • 7.4 Generatingfuzzyrules........................

    • 7.5 Exercisesandprojects ........................



  • 8APPLICATIONS

    • 8.1 Asurveyofindustrialapplications .................

    • 8.2 Coolingschemeforlasermaterials .................

    • 8.3 Colorqualityprocessing .......................

    • 8.4 Identificationoftrashincotton...................

    • 8.5 Integratedpestmanagementsystems................

    • 8.6 Comments...............................

    • Bibliography



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