Foundations of Fuzzy Control

A Practical Approach

Inbunden, Engelska, 2013

1 529 kr

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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 systemAccompanied by a website hosting downloadable MATLAB programsAccompanied by an online course on Fuzzy Control which is taught by the author. Students can access further material and enrol at the companion websiteFoundations 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.

Produktinformation

Utgivningsdatum2013-09-13
Mått177 x 252 x 22 mm
Vikt699 g
FormatInbunden
SpråkEngelska
Antal sidor352
Upplaga2
FörlagJohn Wiley & Sons Inc
ISBN9781118506226

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Jan Jantzen, Adjunct Professor, University of the Aegean, GreeceAs a follow-up on the pioneering work by Holmblad and Østergaard on cement kiln control, Dr Jantzen taught a course on fuzzy control at the Technical University of Denmark (starting 1986). He has participated in the development of a fuzzy controller for a 600 kilowatt screw chiller and a fuzzy idle speed controller for a car engine. At the university he supervised numerous student projects in the process industry, and he teaches a fuzzy control course over the Internet to students and practitioners (www.inference.dk). He also teaches a course on fuzzy logic and intelligent computers at the University of the Aegean, Chios island, Greece (since 2003). Dr Jantzen lives on the island of Samso, Denmark, and he works at the Samso Energy Academy with renewable energy, particularly for European islands.

Foreword xiii Preface to the Second Edition xvPreface to the First Edition xvii1Introduction 11.1 What Is Fuzzy Control? 11.2 Why Fuzzy Control? 21.3 Controller Design 31.4 Introductory Example: Stopping a Car 31.5 Nonlinear Control Systems 91.6 Summary 111.7 The Autopilot Simulator* 121.8 Notes and References* 132 Fuzzy Reasoning 172.1 Fuzzy Sets 172.2 Fuzzy Set Operations 252.3 Fuzzy If–Then Rules 332.4 Fuzzy Logic 362.5 Summary 432.6 Theoretical Fuzzy Logic* 432.7 Notes and References* 533 Fuzzy Control 553.1 The Rule Based Controller 563.2 The Sugeno Controller 613.3 Autopilot Example: Four Rules 643.4 Table Based Controller 653.5 Linear Fuzzy Controller 683.6 Summary 703.7 Other Controller Components* 703.8 Other Rule Based Controllers* 773.9 Analytical Simplification of the Inference* 803.10 Notes and References* 844 Linear Fuzzy PID Control 854.1 Fuzzy P Controller 874.2 Fuzzy PD Controller 894.3 Fuzzy PD+I Controller 904.4 Fuzzy Incremental Controller 924.5 Tuning 944.6 Simulation Example: Third-Order Process 994.7 Autopilot Example: Stable Equilibrium 1014.8 Summary 1034.9 Derivative Spikes and Integrator Windup* 1044.10 PID Loop Shaping* 1064.11 Notes and References* 1095 Nonlinear Fuzzy PID Control 1115.1 Nonlinear Components 1115.2 Phase Plot 1135.3 Four Standard Control Surfaces 1155.4 Fine-Tuning 1185.5 Example: Unstable Frictionless Vehicle 1215.6 Example: Nonlinear Valve Compensator 1245.7 Example: Motor Actuator with Limits 1275.8 Autopilot Example: Regulating a Mass Load 1275.9 Summary 1305.10 Phase Plane Analysis* 1305.11 Geometric Interpretation of the PD Controller* 1345.12 Notes and References* 1436 The Self-Organizing Controller 1456.1 Model Reference Adaptive Systems 1456.2 The Original SOC 1476.3 A Modified SOC 1506.4 Example with a Long Deadtime 1516.5 Tuning and Time Lock 1556.6 Summary 1576.7 Example: Adaptive Control of a First-Order Process* 1576.8 Analytical Derivation of the SOC Adaptation Law* 1616.9 Notes and References* 1697 Performance and Relative Stability 1717.1 Reference Model 1727.2 Performance Measures 1777.3 PID Tuning from Performance Specifications 1807.4 Gain Margin and Delay Margin 1857.5 Test of Four Difficult Processes 1867.6 The Nyquist Criterion for Stability 1887.7 Relative Stability of the Standard Control Surfaces 1917.8 Summary 1937.9 Describing Functions* 1937.10 Frequency Responses of the FPD and FPD+I Controllers* 1987.11 Analytical Derivation of Describing Functions for the Standard Surfaces* 2067.12 Notes and References* 2168 Fuzzy Gain Scheduling Control 2178.1 Point Designs and Interpolation 2188.2 Fuzzy Gain Scheduling 2198.3 Fuzzy Compensator Design 2218.4 Autopilot Example: Stopping on a Hilltop 2268.5 Summary 2288.6 Case Study: the FLS Controller* 2298.7 Notes and References* 2359 Fuzzy Models 2379.1 Basis Function Architecture 2389.2 Handmade Models 2409.3 Machine-Made Models 2499.4 Cluster Analysis 2539.5 Training and Testing 2639.6 Summary 2669.7 Neuro-Fuzzy Models* 2679.8 Notes and References* 27510 Demonstration Examples 27710.1 Hot Water Heater 27710.2 Temperature Control of a Tank Reactor 28210.3 Idle Speed Control of a Car Engine 28710.4 Balancing a Ball on a Cart 29210.5 Dynamic Model of a First-Order Process with a Nonlinearity 30110.6 Summary 30710.7 Further State-Space Analysis of the Cart-Ball System* 30710.8 Notes and References* 314References 315Index 319