Intelligent control systems with LabVIEW

Intelligent Control Systems with LabVIEW™

Pedro Ponce-Cruz • Fernando D. Ramírez-Figueroa

Intelligent Control Systems

with LabVIEW™

123

Pedro Ponce-Cruz, Dr.-Ing.

Fernando D. Ramírez-Figueroa, Research Assistant to Doctor Ponce

Instituto Tecnológico de Estudios Superiores de Monterrey

Campus Ciudad de México

Calle del Puente 222

Col. Ejidos de Huipulco Tlalpan

14380, México, D.F.

México

[email protected]

ISBN 978-1-84882-683-0 e-ISBN 978-1-84882-684-7

DOI 10.1007/978-1-84882-684-7

Springer London Dordrecht Heidelberg New York

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a specific statement, that such names are exempt from the relevant laws and regulations and therefore

free for general use.

The publisher makes no representation, express or implied, with regard to the accuracy of the information

contained in this book and cannot accept any legal responsibility or liability for any errors or omissions

that may be made.

The publisher and the authors accept no legal responsibility for any damage caused by improper use of

the instructions and programs contained in this book and the DVD. Although the software has been tested

with extreme care, errors in the software cannot be excluded.

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Printed on acid-free paper

Springer is part of Springer Science+Business Media (www.springer.com)

This book is dedicated to my mother and son with love.

Pedro Ponce

To my parents Virginia and Fernando because this would not be possible without

their unconditional love and support, and to my advisor and friend, Pedro for giving

me the opportunity to prove myself, and to all those who have accompanied me

along this wonderful journey of knowledge and creation.

David Ramirez

Preface

Control systems are becoming more important every day. At the beginning, the in￾dustry used sequential controls for solving a lot of industrial applications in control

systems, and then the linear systems gave us a huge increase in applying automatic

linear control on industrial application. One of the most recent methods for control￾ling industrial applications is intelligent control, which is based on human behavior

or concerning natural process.

Nowadays, the topic of intelligent control systems has become more than a re￾search subject to the industry. The number of industrial applications is growing ev￾ery day, faster and faster. Thus, new software and hardware platforms are required

in order to design and develop intelligent control systems. The challenge for these

types of systems is to have a novel platform, which allows designing, testing and im￾plementing an intelligent controller system in a short period of time. For the industry

and academy, LabVIEW™ is one of the most important software platforms for de￾veloping engineering applications and could be connected with different hardware

systems, as well as running standalone programs for simulating the controller’s per￾formance (validating the controller by simulation then implementing it). In addition,

LabVIEW is a graphical program that is very easy to learn.

Taking into account these advantages, the software platform described in this

book is LabVIEW from National Instruments™. The book is divided into 7 chapters

and gives all the information required for designing and implementing an intelligent

controller.

Chapter 1 provides an introduction to basic intelligent control concepts and con￾cludes by applying LabVIEW for implementing control systems. Chapter 2 covers

in deep detail the fuzzy logic theory and implementation. This chapter starts with

fundamental fuzzy logic theory for supporting the most important fuzzy logic con￾trollers implemented using LabVIEW.

Chapter 3 deals with artificial neural networks. In this chapter a complete set

of tools for implementing artificial neural networks is presented. Basic examples

of neural networks, such as perceptron, allow the students to understand the most

important topologies in artificial neural networks for modeling and controlling sys￾tems. In Chap. 4 the reader can find neuro-fuzzy controllers, which combine the

vii

viii Preface

fuzzy inference systems with an artificial neural network topology. Thus, the neuro￾fuzzy controllers are an interesting option for modeling and controlling industrial

applications. Chapter 5 discusses genetic algorithms, which are representations of

the natural selection process. This chapter also examines how generic algorithms

can be used as optimization methods. Genetic programming is also explained in

detail.

Chapters 6 and 7 show different algorithms for optimizing and predicting that

could be combined with the conventional intelligent system methodologies pre￾sented in the previous chapters such as fuzzy logic, artificial neural networks and

neuro-fuzzy systems. The methods presented in Chaps. 6 and 7 are: simulated an￾nealing, fuzzy clustering means, partition coefficients, tabu search and predictors.

Supplemental materials supporting the book are available in the companion

DVD. The DVD includes all the LabVIEW programs (VIs) presented inside the

book for intelligent control systems.

This book would never have been possible without the help of remarkable people

who believed in this project. I am not able to acknowledge all of them here, but I

would like to thank Eloisa Acha, Gustavo Valdes, Jeannie Falcon, Javier Gutierrez

and others at National Instruments for helping us to develop a better book.

Finally, I would like to thank the Instituto Tecnológico de Monterrey campus

Ciudad de México for supporting this research project. I wish to remember all my

friends and colleagues who gave me support during this research journey.

ITESM-CCM Dr. Pedro Ponce-Cruz

México City

Contents

1 Intelligent Control for LabVIEW ................................ 1

1.1 Introduction . . ............................................. 1

1.2 Intelligent Control in Industrial Applications . . . . ................ 3

1.3 LabVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Fuzzy Logic ................................................... 9

2.1 Introduction . . ............................................. 9

2.2 Industrial Applications . . .................................... 9

2.3 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3.1 Uncertainty in Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3.2 Concept of Fuzziness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 Foundations of Fuzzy Set Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4.1 Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4.2 Boolean Operations and Terms . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.4.3 Fuzzy Operations and Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4.4 Properties of Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4.5 Fuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4.6 Extension Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.4.7 Alpha Cuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4.8 The Resolution Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.4.9 Fuzziness of Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.4.10 Possibility and Probability Theories . . . . . . . . . . . . . . . . . . . . . 25

2.5 Fuzzy Logic Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.5.1 From Classical to Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.5.2 Fuzzy Logic and Approximate Reasoning . . . . . . . . . . . . . . . . 26

2.5.3 Fuzzy Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.5.4 Properties of Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.5.5 Max–Min Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.5.6 Max–Star Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.5.7 Max–Average Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

ix

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2.6 Fuzzy Linguistic Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.7 The Fuzzy Logic Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.7.1 Linguistic Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.7.2 Membership Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.7.3 Rules Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.7.4 Mamdani Fuzzy Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.7.5 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.7.6 Fuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.7.7 Rules Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.7.8 Defuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.7.9 Tsukamoto Fuzzy Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.7.10 Takagi–Sugeno Fuzzy Controller . . . . . . . . . . . . . . . . . . . . . . . 36

2.7.11 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.7.12 Fuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.7.13 Rules Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.7.14 Crisp Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.8 Implementation of the Fuzzy Logic Controllers

Using the Intelligent Control Toolkit for LabVIEW . . . . . . . . . . . . . . 37

2.8.1 Fuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.8.2 Rules Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.8.3 Defuzzification: Crisp Outputs . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.9 Classical Control Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Futher Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.2 Artificial Neural Network Classification . . . . . . . . . . . . . . . . . . . . . . . . 55

3.3 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.3.1 Perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.3.2 Multi-layer Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.3.3 Trigonometric Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . 71

3.3.4 Kohonen Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.3.5 Bayesian or Belief Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . 84

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Futher Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4 Neuro-fuzzy Controller Theory and Application . . . . . . . . . . . . . . . . . . . 89

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.2 The Neuro-fuzzy Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

4.2.1 Trigonometric Artificial Neural Networks . . . . . . . . . . . . . . . . 91

4.2.2 Fuzzy Cluster Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

4.2.3 Predictive Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

4.2.4 Results Using the Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

4.2.5 Controller Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Contents xi

4.3 ANFIS: Adaptive Neuro-fuzzy Inference Systems . . . . . . . . . . . . . . . 106

4.3.1 ANFIS Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

Futher Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

5 Genetic Algorithms and Genetic Programming . . . . . . . . . . . . . . . . . . . . 123

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

5.1.1 Evolutionary Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

5.2 Industrial Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

5.3 Biological Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.3.1 Search Spaces and Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.3.2 Encoding and Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.4 Genetic Algorithm Stages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.4.1 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

5.4.2 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5.4.3 Crossover. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

5.4.4 Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

5.5 Genetic Algorithms and Traditional Search Methods . . . . . . . . . . . . . 134

5.6 Applications of Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

5.7 Pros and Cons of Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 136

5.8 Selecting Genetic Algorithm Methods . . . . . . . . . . . . . . . . . . . . . . . . . 136

5.9 Messy Genetic Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

5.10 Optimization of Fuzzy Systems Using Genetic Algorithms . . . . . . . . 138

5.10.1 Coding Whole Fuzzy Partitions . . . . . . . . . . . . . . . . . . . . . . . . 138

5.10.2 Standard Fitness Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

5.10.3 Coding Rule Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

5.11 An Application of the ICTL for the Optimization

of a Navigation System for Mobile Robots . . . . . . . . . . . . . . . . . . . . . 140

5.12 Genetic Programming Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

5.12.1 Genetic Programming Definition . . . . . . . . . . . . . . . . . . . . . . . 143

5.12.2 Historical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

5.13 Industrial Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

5.14 Advantages of Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 144

5.15 Genetic Programming Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

5.15.1 Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

5.16 Genetic Programming Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

5.16.1 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

5.16.2 Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

5.16.3 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

5.16.4 Crossover. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

5.16.5 Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

5.17 Variations of Genetic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

5.18 Genetic Programming in Data Modeling . . . . . . . . . . . . . . . . . . . . . . . 150

5.19 Genetic Programming Using the ICTL . . . . . . . . . . . . . . . . . . . . . . . . . 150

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

Futher Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

6 Simulated Annealing, FCM, Partition Coefficients and Tabu Search . 155

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

6.1.1 Introduction to Simulated Annealing . . . . . . . . . . . . . . . . . . . . 156

6.1.2 Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

6.1.3 Introduction to Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

6.1.4 Industrial Applications of Simulated Annealing . . . . . . . . . . . 158

6.1.5 Industrial Applications of Fuzzy Clustering . . . . . . . . . . . . . . 158

6.1.6 Industrial Applications of Tabu Search . . . . . . . . . . . . . . . . . . 158

6.2 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

6.2.1 Simulated Annealing Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 161

6.2.2 Sample Iteration Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

6.2.3 Example of Simulated Annealing

Using the Intelligent Control Toolkit for LabVIEW . . . . . . . . 163

6.3 Fuzzy Clustering Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

6.4 FCM Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

6.5 Partition Coefficients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

6.6 Reactive Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

6.6.1 Introduction to Reactive Tabu Search . . . . . . . . . . . . . . . . . . . . 173

6.6.2 Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

Futher Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

7 Predictors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

7.1 Introduction to Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

7.2 Industrial Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

7.3 Forecasting Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

7.3.1 Qualitative Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

7.3.2 Quantitative Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

7.4 Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

7.5 Exponential Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

7.5.1 Simple-exponential Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . 195

7.5.2 Simple-exponential Smoothing Algorithm . . . . . . . . . . . . . . . 195

7.5.3 Double-exponential Smoothing . . . . . . . . . . . . . . . . . . . . . . . . 196

7.5.4 Holt–Winter Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

7.5.5 Non-seasonal Box–Jenkins Models . . . . . . . . . . . . . . . . . . . . . 198

7.5.6 General Box–Jenkins Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

7.6 Minimum Variance Estimation and Control . . . . . . . . . . . . . . . . . . . . . 200

7.7 Example of Predictors Using the Intelligent Control Toolkit

for LabVIEW (ICTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

7.7.1 Exponential Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

7.7.2 Box–Jenkins Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

7.7.3 Minimum Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

Contents xiii

7.8 Gray Modeling and Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

7.8.1 Modeling Procedure of the Gray System . . . . . . . . . . . . . . . . . 206

7.9 Example of a Gray Predictor Using the ICTL . . . . . . . . . . . . . . . . . . . 207

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

Futher Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211