| An Overview of Fuzzy Control Theory | p. 1 |
| Introduction | p. 1 |
| History | p. 1 |
| Structure of a Fuzzy Controller | p. 3 |
| Fuzzy Modelling Requirements | p. 5 |
| The Fuzzy Controller Block | p. 7 |
| Notations | p. 7 |
| Fuzzy Rule Bases | p. 9 |
| Linguistic Variables | p. 9 |
| Linguistic Hedges | p. 10 |
| Fuzzy Rules | p. 12 |
| Linguistic Variables Revisited | p. 19 |
| The Design of a Fuzzy Controller | p. 20 |
| Choice of Rules | p. 20 |
| Design Parameters | p. 24 |
| Aggregation and Implication Operators | p. 26 |
| t-norms and t-conorms | p. 27 |
| Extension of Logic | p. 28 |
| Conjunction and Disjunction Operators | p. 30 |
| Implication Operators | p. 33 |
| Defuzzification Operators | p. 37 |
| Criteria | p. 37 |
| Overview of the Different Defuzzification Operators | p. 43 |
| An Extended Example | p. 49 |
| Simplified Controllers | p. 52 |
| Table-Based Controllers | p. 53 |
| Sugeno Controllers | p. 55 |
| Adaptive Fuzzy Control | p. 57 |
| General Remarks | p. 57 |
| Scaling | p. 59 |
| Membership Function Tuning using Performance Criteria | p. 60 |
| Gradient Descent Method | p. 64 |
| Self-Organizing Controllers | p. 66 |
| Stability Analysis | p. 69 |
| General Remarks | p. 69 |
| The Input-Output Mapping | p. 70 |
| The State Space Approach | p. 76 |
| Lyapunov Stability | p. 78 |
| Input-Output Stability and Related Techniques | p. 87 |
| Other Adaptive Techniques | p. 88 |
| Neural Networks | p. 89 |
| Neuro-fuzzy Hybrid Systems | p. 107 |
| Genetic Algorithms | p. 120 |
| Fuzzy-Genetic Hybrid Systems | p. 129 |
| References | p. 132 |
| Optimal Fuzzy Management of Reservoir based on Genetic Algorithm | p. 139 |
| Introduction | p. 139 |
| Reservoir Water Release Policy | p. 142 |
| Mathematical Model of the Reservoir | p. 142 |
| Volume Balance Equation | p. 142 |
| Hybrid Dynamical Model of the Reservoir | p. 144 |
| Fuzzy Decision System | p. 144 |
| Optimizing the Decision Strategy | p. 147 |
| Genetic Algorithm and Fuzzy Membership Function Parameters | p. 147 |
| Performances Indices | p. 148 |
| Inflow Identification and Montecarlo Simulation | p. 149 |
| Case Study | p. 154 |
| Conclusions | p. 157 |
| References | p. 158 |
| Genetic Fuzzy Modeling of Supervisory Scheduling of Freight Rail Systems | p. 161 |
| Introduction | p. 161 |
| Genetic Fuzzy Algorithm | p. 164 |
| Supervisory Train Schedule | p. 172 |
| Conclusion | p. 178 |
| References | p. 179 |
| Multiobjective Evolutionary Search of Difference Equations-based Models for Understanding Chaotic Systems | p. 181 |
| Introduction | p. 181 |
| Evolutionary Transparent Modeling of Chaotic Systems | p. 183 |
| Operators Used in the Evolutionary Searches | p. 185 |
| Representation of an Individual | p. 185 |
| Random Generation of Genotypes | p. 186 |
| Genetic Crossover and Mutation | p. 186 |
| Fitness Function | p. 187 |
| Detailed Description of the MOSA Algorithm | p. 188 |
| Outline of the Algorithm | p. 188 |
| The Distance Operator | p. 189 |
| The Selection Operator | p. 190 |
| Example of a MOSA Evolution | p. 190 |
| Experiment and Results | p. 190 |
| Dynamic Behavior of Universal Approximators | p. 190 |
| Benchmark Problems | p. 192 |
| Concluding Remarks and Future Work | p. 198 |
| References | p. 199 |
| An Integrated Fuzzy Inference-based Monitoring, Diagnostic, and Prognostic System for Intelligent Control and Maintenance | p. 203 |
| Introduction | p. 203 |
| Reliability Engineering Methods | p. 204 |
| Integrated Framework | p. 204 |
| Nonparametric Fuzzy Inference System | p. 206 |
| Embodiments of the NFIS | p. 208 |
| Prediction | p. 209 |
| Detection | p. 210 |
| Diagnosis | p. 210 |
| Prognosis | p. 212 |
| Methodology | p. 215 |
| Results | p. 217 |
| Monitoring | p. 217 |
| Diagnosis | p. 218 |
| Prognosis | p. 219 |
| Conclusions | p. 220 |
| References | p. 220 |
| Stable Anti-Swing Control for an Overhead Crane with Velocity Estimation and Fuzzy Compensation | p. 223 |
| Introduction | p. 223 |
| Preliminaries | p. 225 |
| Anti-Swing Control for the Overhead Crane | p. 227 |
| Position Control with Fuzzy Compensation | p. 228 |
| PD Control with a Velocity Observer | p. 229 |
| Stability Analysis | p. 231 |
| Experimental Comparisons | p. 232 |
| Conclusion | p. 235 |
| Appendix | p. 237 |
| References | p. 239 |
| Intelligent Fuzzy PID Controller | p. 241 |
| Introduction | p. 242 |
| The Development of Self-Organizing Fuzzy PID Controller | p. 243 |
| Kinematics and Dynamics of the Robot-Arm | p. 248 |
| Computer Simulation Results | p. 249 |
| Conclusion | p. 258 |
| References | p. 259 |
| Stability Analysis and Performance Design for Fuzzy Model-based Control Systems using a BMI-based Approach | p. 261 |
| Introduction | p. 262 |
| Fuzzy Model and Fuzzy Controller | p. 263 |
| Fuzzy Model | p. 264 |
| Fuzzy Controller | p. 264 |
| Stability Analysis | p. 265 |
| Design of G[subscript j] and a[subscript j] for the Fuzzy Controller | p. 269 |
| Design of Feedback Gains | p. 269 |
| Solution Solving | p. 270 |
| BMI-Based Performance Design of Fuzzy Model-Based Control System | p. 271 |
| Simulation Examples | p. 274 |
| Simulation Example 1 | p. 274 |
| Simulation Example 2 | p. 275 |
| Conclusion | p. 280 |
| References | p. 281 |
| Two-Level Tuning of Fuzzy PID Controllers for Multivariable Process Systems | p. 283 |
| Introduction | p. 284 |
| System Description | p. 286 |
| Two-Level Tuning | p. 287 |
| Low-Level Tuning: Linear PID Controller Tuning | p. 288 |
| Tuning First Loop | p. 289 |
| Tuning ith loop | p. 291 |
| High-Level Tuning: Nonlinearity Tuning | p. 291 |
| Standard Additive Model (SAM) | p. 292 |
| SAM Theorem | p. 294 |
| Fuzzy PID (FPID) Configurations | p. 295 |
| High-Level Nonlinear Tuning Variables | p. 296 |
| Design of SAM | p. 297 |
| Stability Analysis | p. 298 |
| Direct Nyquist Array (DNA) Stability Theorem | p. 298 |
| Maximum Values of PID Parameters | p. 299 |
| Control Simulation | p. 302 |
| Example 1 | p. 303 |
| Example 2 | p. 303 |
| Performance Analysis | p. 306 |
| Conclusions | p. 310 |
| References | p. 310 |
| Evaluation of Fuzzy Implications and Intuitive Criteria of GMP and GMT using MATLAB GUI | p. 313 |
| Introduction | p. 313 |
| Intuitive Criteria of GMP and GMT | p. 314 |
| Fuzzy Implication Methods | p. 316 |
| Properties of Interpretations of Fuzzy IF-THEN Rules | p. 319 |
| Study of Satisfaction of Criteria using MATLAB/Graphics | p. 320 |
| Discussions | p. 385 |
| Conclusions | p. 385 |
| References | p. 385 |
| FzController: A Development Environment for Fuzzy Controllers | p. 387 |
| Introduction | p. 387 |
| General Conception of the FzController System | p. 388 |
| Exact method | p. 388 |
| Approximated Method | p. 389 |
| Modules in FzController | p. 390 |
| Identification Module | p. 390 |
| Design of Fuzzy Controllers Module | p. 391 |
| Real-Time Control Module | p. 396 |
| Automatic Generation of Codes Module | p. 397 |
| Conclusions | p. 400 |
| References | p. 401 |
| A Consistency Criterion for Optimizing Defuzzification in Fuzzy Control | p. 403 |
| Introduction | p. 403 |
| MOM- and COG-defuzzification | p. 409 |
| Single Controller | p. 409 |
| Two Single Disjoint Controllers | p. 411 |
| Two Subcentrally Overlapping Controllers | p. 412 |
| Two Supercentrally Overlapping Controllers | p. 416 |
| Overlapping Controllers with Border Conditions | p. 420 |
| The Consistency Criterion | p. 422 |
| Example | p. 423 |
| BADD-defuzzification | p. 427 |
| Results with No Border Constraints | p. 428 |
| Results with Border Constraints | p. 429 |
| Conclusions | p. 430 |
| References | p. 431 |
| An Asymptotic Consistency Criterion for Optimizing Defuzzification in Fuzzy Control | p. 433 |
| Introduction | p. 433 |
| Rule Antecedent Bases | p. 434 |
| Rule Base Sequences | p. 437 |
| The Asymptotic Consistency Criterion | p. 440 |
| MOM-defuzzification | p. 441 |
| COG-defuzzification | p. 444 |
| BADD-defuzzification | p. 447 |
| Defuzzification Fitness Comparison | p. 448 |
| Conclusions | p. 455 |
| References | p. 455 |
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