Intelligent Control. 4^ Springer. A Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms. Nazmul Siddique.

Transcription

1 Nazmul Siddique Intelligent Control A Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms Foreword by Bernard Widrow 4^ Springer

2 Contents 1 Introduction Intelligent Control Intelligent Control Architecture Approaches to Intelligent Control Experimental Rig of Flexible Arm Overview of the Book 7 References 8 2 Dynamical Systems Introduction Dynamics of Robot Manipulator Dynamics of Flexible-Arm Strength and Stiffness Safety Factor Experimental Flexible Arm Printed Armature Motor Motor Drive Amplifier Accelerometer Computer Interfacing Operating Characteristics Previous Research and Developments Dynamic Equations of Flexible Robotic Ann Development of the Simulation Algorithm Hub Displacement End-Point Displacement Matrix Formulation State-Space Formulation Some Simulation Results Bang-Bang Signal Summary 36 References 36 xiii

3 x v Contents 3 Control Systems Introduction Control Systems Control of Flexible Arm Open-Loop Control Closed-Loop Control Joint Based Collocated Controller Hybrid Collocated and Non-Collocated Controller Alternative Control Approaches Intelligent Control Approaches Summary 53 References 53 4 Mathematics of Fuzzy Control Fuzzy Logic Fuzzy Sets Membership Functions Piecewise Linear MF Nonlinear Smooth MF Sigmoidal MF Polynomial or Spline-Based Functions Irregular Shaped MF Linguistic Variables Features of Linguistic Variables Linguistic Hedges Fuzzy If-then Rules Fuzzy Proposition Methods for Construction of Rule-Base Properties of Fuzzy Rules Fuzzification Inference Mechanism Mamdani Fuzzy Inference Sugeno Fuzzy Inference Tsukamoto Fuzzy Inference Defuzzification Defuzzification Methods Properties of Defuzzification Analysis of Defuzzification Methods Summary 90 References 90 5 Fuzzy Control Introduction Fuzzification for Control 96

4 Contents xv Inference Mechanism for Control Rule-Base for Control Defuzzification for Control Theoretical Analysis of Fuzzy Controllers Consideration of Process Variables Types of Fuzzy Controllers Fuzzy Controller for Flexible Arm 108 Ill Input-Output Selection PD-Like Fuzzy Logic Controller Ill PD-Like Fuzzy Controller with Error and Change of Error PD-Like Fuzzy Controller with Error and. Velocity. 5.5 Pi-Like Fuzzy Controller Integral Windup Action PID-Like Fuzzy Controller PD-PI-Type-like Fuzzy Controller Some Experimental Results on PD-PI FLC Choice of Scaling Factors Summary 132 References Evolutionary-Fuzzy Control Introduction Overview of Evolutionary Algorithms Evolutionary Programming Evolution Strategies Genetic Programming Differential Evolution Cultural Algorithm Genetic Algorithm Evolutionary Fuzzy Control Merging MFs and Rule-Bases of PD-PI FLC Optimising FLC Parameters Using GA Encoding Chromosome Representation for MFs Chromosome Representation Scheme 157 for Rule-Base Objective Function Dynamic Crossover Dynamic Mutation Selection Initialisation Evaluation 166

5 xvi Contents 6.6 Some Experimental Results Summary 173 References Neuro-Fuzzy Control Introduction Neural Networks and Architectures Combinations of Neural Networks and Fuzzy Controllers NN for Correcting FLC NN for Learning Rules NN for Determining MFs NN for Learning/Tuning Scaling Parameters Scaling Parameters of PD-PI Fuzzy Controller Reducing the Number of Scaling Parameters Neural Network for Tuning Scaling Factors Backpropagation Learning with LinearActivation Function Learning with Non-Linear Activation Function Multi-Resolution Learning Adaptive Neural Activation Functions Some Experimental Results Summary 212 References Evolutionary-Neuro-Fuzzy Control Introduction Integration of Fuzzy Systems, Neural Networks and Evolutionary Algorithms EA-NN Cooperative Combination EA for Weight Learning EA for Weights and Activation Functions Learning Optimal Sigmoid Function Shape Learning Evolutionary-Neuro-Fuzzy PD-PI-like Controller GA-Based Neuro-Fuzzy Controller Some Experimental Results Summary 240 References Stability Analysis of Intelligent Controllers Introduction Mathematical Preliminaries Qualitative Stability Analysis of Fuzzy Controllers 252

6 Contents xvii 9.4 Passivity Approach to Stability Analysis of Fuzzy Controllers Stability Analysis of PD-PI-like Fuzzy Controller Summary References Future Work Epilogue Future Research Directions Adaptive Neural Network Control Adaptive Neuro-Fuzzy Controller B-Spline Neural Network CMAC Network Binary Neural Network-Based Fuzzy Controller Summary 279 References 279 Index 281