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Foundations of Fuzzy Control : a Practical Approach.
Foundations of Fuzzy Control 2nd Edition: A Practical Approach has been significantly revised and updated, with two new chapters on Gain Scheduling Control and Neurofuzzy Modelling. It focuses on the PID (Proportional, Integral, Derivative) type controller which is the most widely used in industry a...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2013.
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| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- FOUNDATIONS OF FUZZY CONTROL; Contents; Foreword; Preface to the Second Edition; Preface to the First Edition; 1 Introduction; 1.1 What Is Fuzzy Control?; 1.2 Why Fuzzy Control?; 1.3 Controller Design; 1.4 Introductory Example: Stopping a Car; 1.5 Nonlinear Control Systems; 1.6 Summary; 1.7 The Autopilot Simulator*; 1.8 Notes and References*; 2 Fuzzy Reasoning; 2.1 Fuzzy Sets; 2.1.1 Classical Sets; 2.1.2 Fuzzy Sets; 2.1.3 Universe; 2.1.4 Membership Function; 2.1.5 Possibility; 2.2 Fuzzy Set Operations; 2.2.1 Union, Intersection, and Complement; 2.2.2 Linguistic Variables; 2.2.3 Relations.
- 2.3 Fuzzy If-Then Rules2.3.1 Several Rules; 2.4 Fuzzy Logic; 2.4.1 Truth-Values; 2.4.2 Classical Connectives; 2.4.3 Fuzzy Connectives; 2.4.4 Triangular Norms; 2.5 Summary; 2.6 Theoretical Fuzzy Logic*; 2.6.1 Tautologies; 2.6.2 Fuzzy Implication; 2.6.3 Rules of Inference; 2.6.4 Generalized Modus Ponens; 2.7 Notes and References*; 3 Fuzzy Control; 3.1 The Rule Based Controller; 3.1.1 Rule Base Block; 3.1.2 Inference Engine Block; 3.2 The Sugeno Controller; 3.3 Autopilot Example: Four Rules; 3.4 Table Based Controller; 3.5 Linear Fuzzy Controller; 3.6 Summary; 3.7 Other Controller Components*
- 3.7.1 Controller Components3.8 Other Rule Based Controllers*; 3.8.1 The Mamdani Controller; 3.8.2 The FLS Controller; 3.9 Analytical Simplification of the Inference*; 3.9.1 Four Rules; 3.9.2 Nine Rules; 3.10 Notes and References*; 4 Linear Fuzzy PID Control; 4.1 Fuzzy P Controller; 4.2 Fuzzy PD Controller; 4.3 Fuzzy PD+I Controller; 4.4 Fuzzy Incremental Controller; 4.5 Tuning; 4.5.1 Ziegler-Nichols Tuning; 4.5.2 Hand-Tuning; 4.5.3 Scaling; 4.6 Simulation Example: Third-Order Process; 4.7 Autopilot Example: Stable Equilibrium; 4.7.1 Result; 4.8 Summary.
- 4.9 Derivative Spikes and Integrator Windup*4.9.1 Setpoint Weighting; 4.9.2 Filtered Derivative; 4.9.3 Anti-Windup; 4.10 PID Loop Shaping*; 4.11 Notes and References*; 5 Nonlinear Fuzzy PID Control; 5.1 Nonlinear Components; 5.2 Phase Plot; 5.3 Four Standard Control Surfaces; 5.4 Fine-Tuning; 5.4.1 Saturation in the Universes; 5.4.2 Limit Cycle; 5.4.3 Quantization; 5.4.4 Noise; 5.5 Example: Unstable Frictionless Vehicle; 5.6 Example: Nonlinear Valve Compensator; 5.7 Example: Motor Actuator with Limits; 5.8 Autopilot Example: Regulating a Mass Load; 5.9 Summary; 5.10 Phase Plane Analysis*
- 5.10.1 Trajectory in the Phase Plane5.10.2 Equilibrium Point; 5.10.3 Stability; 5.11 Geometric Interpretation of the PD Controller*; 5.11.1 The Switching Line; 5.11.2 A Rule Base for Switching; 5.12 Notes and References*; 6 The Self-Organizing Controller; 6.1 Model Reference Adaptive Systems; 6.2 The Original SOC; 6.2.1 Adaptation Law; 6.3 A Modified SOC; 6.4 Example with a Long Deadtime; 6.4.1 Tuning; 6.4.2 Adaptation; 6.4.3 Performance; 6.5 Tuning and Time Lock; 6.5.1 Tuning of the SOC Parameters; 6.5.2 Time Lock; 6.6 Summary; 6.7 Example: Adaptive Control of a First-Order Process*