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Fuzzy control and identification / John H. Lilly

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Hoboken, N.J : Wiley, c2010Edition: Online-AusgDescription: Online-Ressource (1 online resource (xv, 231 p.)) : illISBN:
  • 9781282883628
  • 1282883623
  • 9780470872710
Subject(s): Additional physical formats: 9780470542774 | 0470542772 | 9780470872710 | 128288316X | Erscheint auch als: Fuzzy control and identification. Druck-Ausgabe Hoboken, NJ : Wiley, 2010. XV, 230 S.DDC classification:
  • 629.8
MSC: MSC: *93-02 | 93C42 | 03E72 | 93B40RVK: RVK: ZQ 5240LOC classification:
  • TJ213
Online resources: Summary: A comprehensive introduction to fuzzy control and identification, covering both Mamdani and Takagi-Sugeno fuzzy systems A fuzzy control system is a control system based on fuzzy logic, which is a mathematical system that makes decisions using human reasoning processes. This book presents an introductory-level exposure to two of the principal uses for fuzzy logic-identification and control. Drawn from the author's lectures presented in a graduate-level course over the past decade, this volume serves as a holistically suitable single text for a fuzzy control course, compiling the information often found in several different books on the subject into one. Starting with explanations of fuzzy logic, fuzzy control, and adaptive fuzzy control, the book introduces the concept of expert knowledge, which is the basis for much of fuzzy control. From there, the author covers: Basic concepts of fuzzy sets such as membership functions, universe of discourse, linguistic variables, linguistic values, support, a-cut, and convexity Both Mamdani and Takagi-Sugeno fuzzy systems, showing how an effective controller can be designed for many complex nonlinear systems without mathematical models or knowledge of control theory while also suggesting several approaches to modeling of complex engineering systems with unknown models How PID controllers can be made fuzzy and why this is useful Position-form and incremental-form fuzzy controllers How nonlinear systems can be modeled as fuzzy systems in several forms How fuzzy tracking control and model reference control can be realized for nonlinear systems using parallel distributed techniques The estimation of nonlinear systems using the batch least squares, recursive least squares, and gradient methods The creation of direct and indirect adaptive fuzzy controllers Also included areSummary: FUZZY CONTROL AND IDENTIFICATION -- TABLE OF CONTENTS -- PREFACE -- CHAPTER 1: INTRODUCTION -- 1.1 FUZZY SYSTEMS -- 1.2 EXPERT KNOWLEDGE -- 1.3 WHEN AND WHEN NOT TO USE FUZZY CONTROL -- 1.4 CONTROL -- 1.5 INTERCONNECTION OF SEVERAL SUBSYSTEMS -- 1.6 IDENTIFICATION AND ADAPTIVE CONTROL -- 1.7 SUMMARY -- EXERCISES -- CHAPTER 2: BASIC CONCEPTS OF FUZZY SETS -- 2.1 FUZZY SETS -- 2.2 USEFUL CONCEPTS FOR FUZZY SETS -- 2.3 SOME SET-THEORETIC AND LOGICAL OPERATIONS ON FUZZY SETS -- 2.4 EXAMPLE -- 2.5 SINGLETON FUZZY SETS -- 2.6 SUMMARY -- EXERCISES -- CHAPTER 3: MAMDANI FUZZY SYSTEMS -- 3.1 IF-THEN RULES AND RULE BASE -- 3.2 FUZZY SYSTEMS -- 3.3 FUZZIFICATION -- 3.4 INFERENCE -- 3.5 DEFUZZIFICATION -- 3.5.1 Center of Gravity (COG) Defuzzification -- 3.5.2 Center Average (CA) Defuzzification -- 3.6 EXAMPLE: FUZZY SYSTEM FOR WIND CHILL -- 3.6.1 Wind Chill Calculation, Minimum T-Norm, COG Defuzzification -- 3.6.2 Wind Chill Calculation, Minimum T-Norm, CA Defuzzification -- 3.6.3 Wind Chill Calculation, Product T-Norm, COG Defuzzification -- 3.6.4 Wind Chill Calculation, Product T-Norm, CA Defuzzification -- 3.6.5 Wind Chill Calculation, Singleton Output Fuzzy Sets, Product T-Norm, CA Defuzzification -- 3.7 SUMMARY -- EXERCISES -- CHAPTER 4: FUZZY CONTROL WITH MAMDANI SYSTEMS -- 4.1 TRACKING CONTROL WITH A MAMDANI FUZZY CASCADE COMPENSATOR -- 4.1.1 Initial Fuzzy Compensator Design: Ball and Beam Plant -- 4.1.2 Rule Base Determination: Ball and Beam Plant -- 4.1.3 Inference: Ball and Beam Plant -- 4.1.4 Defuzzification: Ball and Beam Plant -- 4.2 TUNING FOR IMPROVED PERFORMANCE BY ADJUSTING SCALING GAINS -- 4.3 EFFECT OF INPUT MEMBERSHIP FUNCTION SHAPES -- 4.4 CONVERSION OF PID CONTROLLERS INTO FUZZY CONTROLLERS -- 4.4.1 Redesign for Increased Robustness -- 4.5 INCREMENTAL FUZZY CONTROL -- 4.6 SUMMARY -- EXERCISES.PPN: PPN: 809137291Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PQE
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