Foundations of Fuzzy Control A Practical Approach 2nd Edition by Jan Jantzen 1118506227 9781118506226

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ISBN 10: 1118506227

ISBN 13: 9781118506226

Author: Jan Jantzen

Foundations of Fuzzy Control: A Practical Approach, 2nd Edition 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 and systematically analyses several fuzzy PID control systems and adaptive control mechanisms. This new edition covers the basics of fuzzy control and builds a solid foundation for the design of fuzzy controllers, by creating links to established linear and nonlinear control theory. Advanced topics are also introduced and in particular, common sense geometry is emphasised. Key features Sets out practical worked through problems, examples and case studies to illustrate each type of control system Accompanied by a website hosting downloadable MATLAB programs Accompanied by an online course on Fuzzy Control which is taught by the author. Students can  access further material and enrol at the companion website Foundations of Fuzzy Control: A Practical Approach, 2nd Edition is an invaluable resource for researchers, practitioners, and students in engineering. It is especially relevant for engineers working with automatic control of mechanical, electrical, or chemical systems. 

Foundations of Fuzzy Control A Practical Approach 2nd Table of contents:

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 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 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 Components

3.8 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 Plane

5.10.2 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*

6.7.1 The 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 The Fuzzy Controller

6.9 Notes and References*

7 Performance and Relative Stability

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 The 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 The 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 Fuzzy Gain Scheduling Control

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 The FLS Design Procedure

8.7 Notes and References*

9 Fuzzy Models

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 Demonstration Examples

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*

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