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Intelligent control design and MATLAB simulation

Author: Jinkun Liu
Publisher: Singapore : Springer, [2018] ©2018
Edition/Format:   eBook : Document : EnglishView all editions and formats
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Liu, Jinkun, 1965-
Intelligent control design and MATLAB simulation.
Singapore : Springer, 2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jinkun Liu
ISBN: 9789811052637 9811052638
OCLC Number: 1004673054
Description: 1 online resource (xv, 290 pages) : illustrations
Contents: 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.
Responsibility: Jinkun Liu.

Abstract:

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.

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