Fuzzy Modeling and Fuzzy Control (Hardcover)
暫譯: 模糊建模與模糊控制 (精裝版)

Huaguang Zhang, Derong Liu

Fuzzy Modeling and Fuzzy Control (Hardcover)
  • 出版商: Birkhauser Boston
  • 出版日期: 2006-09-26
  • 售價: $1,350
  • 貴賓價: 9.8 折 $1,323
  • 語言: 英文
  • 頁數: 416
  • 裝訂: Hardcover
  • ISBN: 0817644911
  • ISBN-13: 9780817644918
  • 相關分類: 控制系統 Control-systems

    • 下單後立即進貨 (約5~7天)

買這商品的人也買了...

相關主題

商品描述

Description

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has been applied to many real-world problems, especially in the area of consumer products. This book presents the first unified and thorough treatment of fuzzy modeling and fuzzy control, providing necessary tools for the control of complex nonlinear systems.

Based on three types of fuzzy models—the Mamdani fuzzy model, the Takagi–Sugeno fuzzy model, and the fuzzy hyperbolic model—the book addresses a number of important issues in fuzzy control systems, including fuzzy modeling, fuzzy inference, stability analysis, systematic design frameworks, robustness, and optimality. The authors develop several advanced control schemes, such as the fuzzy model-based generalized predictive control scheme, the fuzzy adaptive control scheme based on fuzzy basis function vectors, the fuzzy control scheme based on fuzzy performance evaluators, and the fuzzy sliding-mode control scheme. Careful consideration is given to questions concerning model complexity, model precision, and computing time.

In addition to being an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, the book may also be appropriate for classroom use in a graduate course in electrical engineering, computer engineering, and computer science. Applied mathematicians, control engineers, computer scientists, and physicists will benefit from the presentation as well.

Table of Contents

Preface  xi 
 1 Fuzzy Set Theory and Rough Set Theory
  1 (32)
 1.1 Classical Set Theory
  2 (2)
 1.2 Fuzzy Set Theory
  4 (22)
 1.3 Rough Set Theory
  26 (5)
 1.4 Summary
  31 (1)
 Bibliography
  31 (2)
 2 Identification of the TakagiSugeno Fuzzy Model
  33 (44)
 2.1 Introduction
  33 (1)
 2.2 Description of the TS Fuzzy Model
  34 (3)
 2.3 An Off-Line Fuzzy Identification Algorithm
  37 (25)
 2.4 An Identification Approach with Less Computational Burden
  62 (6)
 2.5 Identification Approach for the Generalized TS Fuzzy Model
  68 (7)
 2.6 Summary
  75 (1)
 Bibliography
  75 (2)
 3 Fuzzy Model Identification Based on Rough Set Data Analysis
  77 (32)
 3.1 Introduction
  77 (1)
 3.2 Preliminaries
  78 (7)
 3.3 Input Structure Identification
  85 (9)
 3.4 Fuzzy Relation Model Identification
  94 (7)
 3.5 ANN Modeling Based on Rough Sets
  101 (5)
 3.6 Summary
  106 (1)
 Bibliography
  106 (3)
 4 Identification of the Fuzzy Hyperbolic Model
  109 (28)
 4.1 Introduction
  109 (1)
 4.2 Fuzzy Hyperbolic Model
  110 (8)
 4.3 Generalized Fuzzy Hyperbolic Model
  118 (16)
 4.4 Summary
  134 (1)
 Bibliography
  134 (3)
 5 Basic Methods for Fuzzy Inference and Control
  137 (36)
 5.1 Introduction
  137 (1)
 5.2 Design of a Simple Fuzzy Control System
  137 (8)
 5.3 Parameters and Responses of the Simple Fuzzy Control System
  145 (3)
 5.4 Fuzzy Self-Tuning Control
  148 (6)
 5.5 Simulation Comparison Under Disturbances
  154 (5)
 5.6 Robustness of a Fuzzy Self-Tuning Control System
  159 (1)
 5.7 Automatic Generation of a Fuzzy State-Action Table
  159 (12)
 5.8 Summary
  171 (1)
 Bibliography
  171 (2)
 6 Fuzzy Inference and Control Methods Involving Two Kinds of Uncertainties
  173 (22)
 6.1 Introduction
  173 (1)
 6.2 Historical Overview and Problem Description
  174 (1)
 6.3 Definitions of Several Basic Concepts
  175 (5)
 6.4 The Function CF and the Overall Point-Valued THFDP Algorithm
  180 (2)
 6.5 Fuzzy Decision-Making of Composite Rules
  182 (1)
 6.6 Numerical Examples
  183 (2)
 6.7 Fuzzy Control Methods Involving Two Kinds of Uncertainties
  185 (7)
 6.8 Summary
  192 (1)
 Bibliography
  192 (3)
 7 Fuzzy Control Schemes via a Fuzzy Performance Evaluator
  195 (46)
 7.1 Introduction
  195 (1)
 7.2 Fundamentals of a Fuzzy Control Scheme via FPE
  196 (1)
 7.3 Fuzzy Adaptive Control Scheme via FPE
  197 (13)
 7.4 Fuzzy State Feedback Control Scheme via FPE
  210 (14)
 7.5 Fuzzy Control of Nonlinear Systems with Time-Delays via FPE
  224 (15)
 7.6 Summary
  239 (1)
 Bibliography
  239 (2)
 8 Multivariable Predictive Control Based on the TS Fuzzy Model
  241 (32)
 8.1 Introduction
  241 (1)
 8.2 Preliminaries
  242 (2)
 8.3 Equivalent Transformation of the Fuzzy Model
  244 (5)
 8.4 Predictive Control Law for Multivariable Processes
  249 (2)
 8.5 Stability of a Fuzzy Generalized Predictive Control System
  251 (2)
 8.6 Other Performance Analysis
  253 (2)
 8.7 Fuzzy Generalized Predictive Control of a Boiler-Turbine Unit
  255 (4)
 8.8 Comparison of Fuzzy Predictive Control and Conventional Control
  259 (2)
 8.9 Robustness of Fuzzy Generalized Predictive Control System
  261 (2)
 8.10 Fuzzy Modeling of Operators' Control Rules with Application
  263 (6)
 8.11 Summary
  269 (1)
 Bibliography
  270 (3)
 9 Adaptive Control Methods Based on Fuzzy Basis Function Vectors
  273 (26)
 9.1 Introduction
  273 (1)
 9.2 Notation and Preliminaries
  274 (4)
 9.3 Design of an Adaptive Controller Based on Fuzzy Basis Function Vectors for Multivariable Square Nonlinear Systems
  278 (11)
 9.4 Design of an Adaptive Controller Based on Fuzzy Basis Function Vectors for Multivariable Nonsquare Nonlinear Systems
  289 (3)
 9.5 Numerical Example
  292 (4)
 9.6 Summary
  296 (1)
 Bibliography
  296 (3)
10 Controller Design Based on the Fuzzy Hyperbolic Model  299 (24)
 10.1 Introduction
  299 (1)
 10.2 Stable Controller Design by Pole-Placement Method
  300 (5)
 10.3 Nonlinear H2 Optimal Controller Design
  305 (4)
 10.4 H. Controller Design
  309 (3)
 10.5 Control of Nonlinear Time-Delay Systems with Uncertainties
  312 (7)
 10.6 Summary
  319 (1)
 Bibliography
  319 (4)
11 Fuzzy H. Filter Design for Nonlinear Discrete-Time Systems with Multiple Time-Delays  323 (34)
 11.1 Introduction
  323 (1)
 11.2 Modeling of Nonlinear Systems Using the TS Fuzzy System
  324 (4)
 11.3 Fuzzy H. Filtering Analysis Based on the TS Fuzzy Model
  328 (12)
 11.4 Fuzzy H. Filter Design
  340 (6)
 11.5 Simulation Example
  346 (7)
 11.6 Summary
  353 (1)
 Bibliography
  353 (4)
12 Chaotification of the Fuzzy Hyperbolic Model  357 (32)
 12.1 Introduction
  357 (1)
 12.2 Chaotification by the Impulsive Control Method
  358 (9)
 12.3 Chaotification by the Inverse Optimal Control Method
  367 (10)
 12.4 Chaotification of the Original System
  377 (8)
 12.5 Summary
  385 (1)
 Bibliography
  385 (4)
13 Feedforward Fuzzy Control Approach Using the Fourier Integral  389 (24)
 13.1 Introduction
  389 (1)
 13.2 Problem Formation
  390 (2)
 13.3 System Description and Assumptions
  392 (1)
 13.4 FSMC Feedback Control Law
  393 (7)
 13.5 Adaptive Feedforward Controller Design in the Fourier Space
  400 (4)
 13.6 Convergence Conditions of the Global Closed-Loop System
  404 (2)
 13.7 Simulation and Comparisons
  406 (5)
 13.8 Summary
  411 (1)
 Bibliography
  412 (1)
Index  413

商品描述(中文翻譯)

**描述**

模糊邏輯方法已被證明在處理包含不確定性的複雜非線性系統方面是有效的，這些系統在其他情況下難以建模。基於此方法論的技術已應用於許多現實世界的問題，特別是在消費產品領域。本書提供了模糊建模和模糊控制的首次統一且徹底的處理，提供控制複雜非線性系統所需的工具。

本書基於三種類型的模糊模型——Mamdani模糊模型、Takagi–Sugeno模糊模型和模糊雙曲模型——探討了模糊控制系統中的多個重要問題，包括模糊建模、模糊推理、穩定性分析、系統設計框架、魯棒性和最優性。作者開發了幾種先進的控制方案，例如基於模糊模型的廣義預測控制方案、基於模糊基函數向量的模糊自適應控制方案、基於模糊性能評估器的模糊控制方案，以及模糊滑模控制方案。對於模型複雜性、模型精度和計算時間等問題給予了仔細考量。

除了作為電氣、計算機、化學、工業、土木、製造、機械和航空工程師的優秀參考資料外，本書也適合用於電氣工程、計算機工程和計算機科學的研究生課程。應用數學家、控制工程師、計算機科學家和物理學家也將從中受益。

**目錄**

前言 xi
1 模糊集合理論與粗糙集合理論 1 (32)
1.1 古典集合理論 2 (2)
1.2 模糊集合理論 4 (22)
1.3 粗糙集合理論 26 (5)
1.4 總結 31 (1)
參考文獻 31 (2)
2 Takagi-Sugeno模糊模型的識別 33 (44)
2.1 介紹 33 (1)
2.2 TS模糊模型的描述 34 (3)
2.3 一種離線模糊識別算法 37 (25)
2.4 一種計算負擔較小的識別方法 62 (6)
2.5 廣義TS模糊模型的識別方法 68 (7)
2.6 總結 75 (1)
參考文獻 75 (2)
3 基於粗糙集合數據分析的模糊模型識別 77 (32)
3.1 介紹 77 (1)
3.2 預備知識 78 (7)
3.3 輸入結構識別 85 (9)
3.4 模糊關係模型識別 94 (7)
3.5 基於粗糙集合的人工神經網絡建模 101 (5)
3.6 總結 106 (1)
參考文獻 106 (3)
4 模糊雙曲模型的識別 109 (28)
4.1 介紹 109 (1)
4.2 模糊雙曲模型 110 (8)
4.3 廣義模糊雙曲模型 118 (16)
4.4 總結 134 (1)
參考文獻 134 (3)
5 模糊推理與控制的基本方法 137 (36)
5.1 介紹 137 (1)
5.2 簡單模糊控制系統的設計 137 (8)
5.3 簡單模糊控制系統的參數與響應 145 (3)
5.4 模糊自調整控制 148 (6)
5.5 在擾動下的模擬比較 154 (5)
5.6 模糊自調整控制系統的魯棒性 159 (1)
5.7 自動生成模糊狀態-行動表 159 (12)
5.8 總結 171 (1)
參考文獻 171 (2)
6 涉及兩種不確定性的模糊推理與控制方法 173 (22)
6.1 介紹 173 (1)
6.2 歷史概述與問題描述 174 (1)
6.3 幾個基本概念的定義 175 (5)
6.4 函數CF與整體點值THFDP算法 180 (2)
6.5 複合規則的模糊決策 182 (1)
6.6 數值範例 183 (2)
6.7 涉及兩種不確定性的模糊控制方法 185 (7)
6.8 總結 192 (1)
參考文獻 192 (3)
7 通過模糊性能評估器的模糊控制方案 195 (46)
7.1 介紹 195 (1)
7.2 通過FPE的模糊控制方案基礎 196 (1)
7.3 通過FPE的模糊自適應控制方案 197 (13)
7.4 通過FPE的模糊狀態反饋控制方案 210 (14)
7.5 通過FPE的具有時間延遲的非線性系統的模糊控制 224 (15)
7.6 總結 239 (1)
參考文獻 239 (2)
8 基於TS模糊模型的多變量預測控制 241 (32)
8.1 介紹 241 (1)
8.2 預備知識 242 (2)
8.3 模糊模型的等效轉換 244 (5)
8.4 多變量過程的預測控制法則 249 (2)
8.5 模糊廣義預測控制系統的穩定性 251 (2)
8.6 其他性能分析 253 (2)
8.7 鍋爐-渦輪單元的模糊廣義預測控制 255 (4)
8.8 模糊預測控制與傳統控制的比較 259 (2)
8.9 模糊廣義預測控制系統的魯棒性 261 (2)
8.10 操作員控制規則的模糊建模及應用 263 (6)
8.11 總結 269 (1)
參考文獻 270 (3)
9 基於模糊基函數向量的自適應控制方法 273 (26)
9.1 介紹 273 (1)
9.2 符號與預備知識 274 (4)
9.3 基於模糊基函數向量的多變量平方非線性系統自適應控制器設計 278 (11)
9.4 基於模糊基函數向量的多變量非平方非線性系統自適應控制器設計 289 (3)
9.5 數值範例 292 (4)
9.6 總結 296 (1)
參考文獻 296 (3)
10 基於模糊雙曲模型的控制器設計 299 (24)
10.1 介紹 299 (1)
10.2 通過極點配置法的穩定控制器設計 300 (5)
10.3 非線性H2最優控制器設計 305 (4)
10.4 H.控制器設計 309 (3)
10.5 具有不確定性的非線性時間延遲系統的控制 312 (7)
10.6 總結 319 (1)
參考文獻 319 (4)
11 針對具有多重時間延遲的非線性離散時間系統的模糊H.濾波器設計 323 (34)
11.1 介紹 323 (1)
11.2 使用TS模糊系統的非線性系統建模 324 (4)
11.3 基於TS模糊模型的模糊H.濾波分析 328 (12)
11.4 模糊H.濾波器設計 340 (6)
11.5 模擬範例 346 (7)
11.6 總結 353 (1)
參考文獻 353 (4)
12 模糊雙曲模型的混沌化 357 (32)
12.1 介紹 357 (1)
12.2 通過脈衝控制法的混沌化 358 (9)
12.3 通過逆最優控制法的混沌化 367 (10)
12.4 原始系統的混沌化 377 (8)
12.5 總結 385 (1)
參考文獻 385 (4)
13 使用傅立葉積分的前饋模糊控制方法 389 (24)
13.1 介紹 389 (1)
13.2 問題形成 390 (2)
13.3 系統描述與假設 392 (1)
13.4 FSMC反饋控制法則 393 (7)
13.5 在傅立葉空間中的自適應前饋控制器設計 400 (4)
13.6 全局閉環系統的收斂條件 404 (2)
13.7 模擬與比較 406 (5)
13.8 總結 411 (1)
參考文獻 412 (1)
索引 413

類似商品