Foundations of fuzzy control : a practical approach /

Saved in:

Bibliographic Details
Main Author:	Jantzen, Jan
Corporate Author:	Ebooks Corporation
Format:	Electronic eBook
Language:	English
Published:	Chichester, West Sussex, United Kingdom : Wiley, 2013.
Edition:	Second edition.
Subjects:	Automatic control. Fuzzy systems. Fuzzy automata.
Online Access:	Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

Table of Contents:

Machine generated contents note: 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. Autopilot Simulator*
1.8. Notes and References*
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 Rules
2.3.1. 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*
3.1. Rule Based Controller
3.1.1. Rule Base Block
3.1.2. Inference Engine Block
3.2. 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 Components
3.8. Other Rule Based Controllers*
3.8.1. Mamdani Controller
3.8.2. FLS Controller
3.9. Analytical Simplification of the Inference*
3.9.1. Four Rules
3.9.2. Nine Rules
3.10. Notes and References*
4.1. 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*
5.1. 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 Plane
5.10.2. Equilibrium Point
5.10.3. Stability
5.11. Geometric Interpretation of the PD Controller*
5.11.1. Switching Line
5.11.2. Rule Base for Switching
5.12. Notes and References*
6.1. Model Reference Adaptive Systems
6.2. Original SOC
6.2.1. Adaptation Law
6.3. 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*
6.7.1. MIT Rule
6.7.2. Choice of Control Law
6.7.3. Choice of Adaptation Law
6.7.4. Convergence
6.8. Analytical Derivation of the SOC Adaptation Law*
6.8.1. Reference Model
6.8.2. Adjustment Mechanism
6.8.3. Fuzzy Controller
6.9. Notes and References*
7.1. Reference Model
7.2. Performance Measures
7.3. PID Tuning from Performance Specifications
7.4. Gain Margin and Delay Margin
7.5. Test of Four Difficult Processes
7.5.1. Higher-Order Process
7.5.2. Double Integrator Process
7.5.3. Process with a Long Time Delay
7.5.4. Process with Oscillatory Modes
7.6. Nyquist Criterion for Stability
7.6.1. Absolute Stability
7.6.2. Relative Stability
7.7. Relative Stability of the Standard Control Surfaces
7.8. Summary
7.9. Describing Functions*
7.9.1. Static Nonlinearity
7.9.2. Limit Cycle
7.10. Frequency Responses of the FPD and FPD+I Controllers*
7.10.1. FPD Frequency Response with a Linear Control Surface
7.10.2. FPD Frequency Response with Nonlinear Control Surfaces
7.10.3. Fuzzy PD+I Controller
7.10.4. Limit Cycle
7.11. Analytical Derivation of Describing Functions for the Standard Surfaces*
7.11.1. Saturation Surface
7.11.2. Deadzone Surface
7.11.3. Quantizer Surface
7.12. Notes and References*
8.1. Point Designs and Interpolation
8.2. Fuzzy Gain Scheduling
8.3. Fuzzy Compensator Design
8.4. Autopilot Example: Stopping on a Hilltop
8.5. Summary
8.6. Case Study: the FLS Controller*
8.6.1. Cement Kiln Control
8.6.2. High-Level Fuzzy Control
8.6.3. FLS Design Procedure
8.7. Notes and References*
9.1. Basis Function Architecture
9.2. Handmade Models
9.2.1. Approximating a Curve
9.2.2. Approximating a Surface
9.3. Machine-Made Models
9.3.1. Least-Squares Line Fit
9.3.2. Least-Squares Basis Function Fit
9.4. Cluster Analysis
9.4.1. Mahalanobis Distance
9.4.2. Hard Clusters, HCM Algorithm
9.4.3. Fuzzy Clusters, FCM Algorithm
9.5. Training and Testing
9.6. Summary
9.7. Neuro-Fuzzy Models*
9.7.1. Neural Networks
9.7.2. Gradient Descent Algorithm
9.7.3. Adaptive Neuro-Fuzzy Inference System (ANFIS)
9.8. Notes and References*
10.1. Hot Water Heater
10.1.1. Installing a Timer Switch
10.1.2. Fuzzy P Controller
10.2. Temperature Control of a Tank Reactor
10.2.1. CSTR Model
10.2.2. Results and Discussion
10.3. Idle Speed Control of a Car Engine
10.3.1. Engine Model
10.3.2. Results and Discussion
10.4. Balancing a Ball on a Cart
10.4.1. Mathematical Model
10.4.2. Step 1: Design a Crisp PD Controller
10.4.3. Step 2: Replace it with a Linear Fuzzy
10.4.4. Step 3: Make it Nonlinear
10.4.5. Step 4: Fine-Tune it
10.5. Dynamic Model of a First-Order Process with a Nonlinearity
10.5.1. Supervised Model
10.5.2. Semi-Automatic Identification by a Modified HCM
10.6. Summary
10.7. Further State-Space Analysis of the Cart-Ball System*
10.7.1. Nonlinear Equations
10.8. Notes and References*.

Foundations of fuzzy control : a practical approach /

Similar Items