Intelligent control design and MATLAB simulation / Jinkun Liu.
Liu, Jinkun, 1965- author.
2018
TJ217.5 .L585 2018eb
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Title Intelligent control design and MATLAB simulation / Jinkun Liu.
Author Liu, Jinkun, 1965- author.
ISBN 9789811052637 (electronic book)
9811052638 (electronic book)
9789811052620
981105262X
DOI https://doi.org/10.1007/978-981-10-5263-7
Published Singapore : Springer, [2018]
Copyright ©2018
Language English
Description 1 online resource (xv, 290 pages) : illustrations
Other Standard Identifiers 10.1007/978-981-10-5263-7 doi
Call Number TJ217.5 .L585 2018eb
Dewey Decimal Classification 629.8
Summary This book offers a comprehensive introduction to intelligent control system design, using MATLAB simulation to verify typical intelligent controller designs. It also uses real-world case studies that present the results of intelligent controller implementations to illustrate the successful application of the theory. Addressing the need for systematic design approaches to intelligent control system design using neural network and fuzzy-based techniques, the book introduces the concrete design method and MATLAB simulation of intelligent control strategies; offers a catalog of implementable intelligent control design methods for engineering applications; provides advanced intelligent controller design methods and their stability analysis methods; and presents a sample simulation and Matlab program for each intelligent control algorithm. The main topics addressed are expert control, fuzzy logic control, adaptive fuzzy control, neural network control, adaptive neural contro l and intelligent optimization algorithms, providing several engineering application examples for each method.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Description based on print version record.
Available in Other Form Intelligent control design and MATLAB simulation.
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Record Appears in Online Resources > Ebooks
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Preface; Contents; About the Author; Abstract; 1 Introduction to Intelligent Control; 1.1 Expert Control; 1.2 Fuzzy Logic Control; 1.3 Neural Network and Control; 1.4 Intelligent Search Algorithm; References; 2 Expert PID Control; 2.1 Expert PID Control; 2.2 Simulation Example; Reference; 3 Foundation of Fuzzy Mathematics; 3.1 Characteristic Function and Membership Function; 3.2 Fuzzy Set Expression; 3.3 Calculation Method of Fuzzy Set; 3.3.1 Basic Calculation Method of Fuzzy Set; 3.3.2 Fuzzy Operator; 3.3.3 Typical Membership Function; 3.3.4 Design of Fuzzy System.

3.4 Fuzzy Matrix Calculation3.4.1 Fuzzy Matrix; 3.4.2 Fuzzy Matrix Calculation; 3.4.3 Compound of Fuzzy Matrix; 3.5 Fuzzy Inference; 3.6 Fuzzy Equation; Reference; 4 Fuzzy Logic Control; 4.1 Design of Fuzzy Logic Controller; 4.2 An Example for a Fuzzy Logic Controller Design; 4.3 Fuzzy Logic Control for Washing Machine; 4.4 Fuzzy PI Control; 4.4.1 PI Tuning Controller with Fuzzy Logic; 4.4.2 Simulation Example; References; 5 Fuzzy T-S Modeling and Control; 5.1 Fuzzy T-S Model; 5.2 Fuzzy T-S Modeling and Control Based on LMI; 5.2.1 Controller Design of T-S Fuzzy Model Based on LMI.

5.2.2 LMI Design and Analysis5.2.3 Transformation of LMI; 5.2.4 LMI Design Example; 5.3 Fuzzy T-S Modeling and Control Based on LMI for Inverted Pendulum; 5.3.1 System Description; 5.3.2 Simulation Based on Two Fuzzy Rules Design; 5.3.3 Simulation Based on Four Fuzzy Rules Design; 5.4 Simulation Example of YALMIP Toolbox; References; 6 Adaptive Fuzzy Control; 6.1 Adaptive Fuzzy Control; 6.2 Fuzzy Approximation; 6.2.1 Fuzzy System Design; 6.2.2 Fuzzy System Approximation; 6.2.3 Simulation Example; 6.2.3.1 One Dimension Function Approximation; 6.2.3.2 Two Dimension Function Approximation.

6.3 Adaptive Fuzzy Controller Design6.3.1 Problem Description; 6.3.2 Fuzzy Approximation; 6.3.3 Adaptive Fuzzy Control Design and Analysis; 6.3.4 Simulation Example; 6.4 Adaptive Fuzzy Control Based on Fuzzy System Compensator; 6.4.1 System Description; 6.4.2 Adaptive Fuzzy Control Design and Analysis; 6.4.3 Only Consider Friction; 6.4.4 Simulation Example; References; 7 Neural Networks; 7.1 Introduction; 7.2 Single Neural Network; 7.3 BP Neural Network Design and Simulation; 7.3.1 BP Network Structure; 7.3.2 Approximation of BP Neural Network; 7.3.3 Simulation Example.

7.4 RBF Neural Network Design and Simulation7.4.1 RBF Algorithm; 7.4.2 RBF Design Example with MATLAB Simulation; 7.4.2.1 For Structure 1-5-1 RBF Neural Network; 7.4.2.2 For Structure 2-5-1 RBF Neural Network; 7.5 RBF Neural Network Approximation Based on Gradient Descent Method; 7.5.1 RBF Neural Network Approximation; 7.5.2 Simulation Example; 7.6 Effects of Analysis on RBF Approximation; 7.6.1 Effects of Gaussian Function Parameters on RBF Approximation; 7.6.2 Effects of Hidden Nets Number on RBF Approximation; 7.7 RBF Neural Network Training for System Modeling.

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