階層式模糊控制及其在倒三角體系統之應用｜國立中央大學博碩士論文系統

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研究生：	李明霖 Ming-Lin Lee
論文名稱：	階層式模糊控制及其在倒三角體系統之應用 Hierarchical Fuzzy Control with Applications to Seesaw Systems
指導教授：	鍾鴻源 Hung-yuan Chung
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 電機工程學系 Department of Electrical Engineering
畢業學年度：	88
語文別：	中文
論文頁數：	60
中文關鍵詞：	階層式模糊控制、倒三角體
外文關鍵詞：	hierarchical fuzzy system, seesaw
相關次數：	點閱：5 下載：0
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通常在設計模糊控制器，包含獲得資料、定義控制結構、定義規則庫及其他的控制參數時，都是相當費時的。目前，有一項重要的議題就是如何減少相關模糊規則的數目，以符合計算上的需求，階層式模糊控制系統的想法因此被提出。可是，在階層化的中間階層裡，相關模糊規則可能只有些許的物理意義而導致難以去控制。而且這種現象在越多階層越明顯。
為了解決這中間階層沒有物理意義的輸出變數，本文提出一個新型式的規則庫對映方案，來求得相關規則而不必考慮其物理意義。如此，所有的規則不必再重新設計，一樣可以達到減少規則數目，而且，在多層架構中，這種對映法一樣有效。
利用電腦模擬來證實本文所提出之方法的可行性，以及說明整個設計過程。再則，對實際系統之實驗，例如倒三角體，輔以基因演算法，更加驗證了這個設計方法的有效性。由這些模擬及實驗結果顯示，本文提出的方法確實提供有效之途徑以設計階層化模糊控制系統。

The design of fuzzy controllers is commonly a time-consuming activity involving knowledge acquisition, definition of the controller structure, definition of rules, and other controller parameters.
At present, one of the important issues in fuzzy logic systems is how to reduce the number of involved rules and their corresponding computation requirements. The idea of hierarchical fuzzy systems (HFSs) has been reported. But, the involved fuzzy rules in the middle of the hierarchical structure have little physical meaning and consequently are hard to design. This phenomenon becomes prominent as the number of layers grows larger in an HFS.
To overcome the problem that intermediate outputs have nothing to do with the physical variables, this thesis propose a new kind of mapping rule base scheme to get the rule base of HFS without the physical meaning. As a result, all of the rule bases of fuzzy logic units (FLUs) don’t design again and we can reduce the number of involved rules. In many layers, the mapping rule is useful, equivalently.
The several simulations on computer are given to confirm the correctness and to illustrate design procedures. Moreover, Experiments on a practical system, such as an inverted wedge system, assisted with genetic algorithm, verify the effectiveness of the proposed methods. Judging from simulative and experimental results, the methods described in this provide efficient approaches to design HFSs.

TABLE OF CONTENT
page
Abstract I
Table of Content II
List of Figures V
List of Tables VII
List of Equations VIII
CHAPTER 1 INTRODUCTION
1-1Background 1
1-2Motivation and Purpose 2
1-3Organization 2
CHAPTER 2 FUZZY CONTROL THEORY
2-1 Overview of Fuzzy Control 3
2-2 A Brief History of Fuzzy Control 4
2-3 Perspective on Fuzzy Control 5
2-3-1 The Basic Structure of the Fuzzy controller 5
2-3-2 Fuzzification 5
2-3-3 Knowledge Base 6
2-3-4 Inference Engine 7
2-3-5 Defuzzification 8
2-4 Algorithm for Design of the Fuzzy Controller 9
CHAPTER 3 HIERARCHICAL FUZZY CONTROL SCHEMES
3-1 Introduction 10
3-2 A Perspective on Hierarchical Fuzzy Control Schemes 10
3-2-1 Conventional Single Layer Fuzzy Logic Systems 10
3-2-2 Hierarchical Fuzzy Logic Systems 11
3-2-3 Structure-Hierarchical Fuzzy Logic Systems 12
3-2-4 Compare with the Result of Output for HFS
and Single Layer Fuzzy Systems 13
3-3 The Basic Concept of Limpid-HFSs 17
3-3-1 Introduction 17
3-3-2 Limpid-hierarchical Fuzzy Systems ( L-HFSs ) 18
3-3-3 Algorithm for Limpid-HFSs 21
3-3-4 Example 22
3-3-5 The constraint of the reducing number of rules 24
3-3-6 Conclusion 24
CHAPTER 4 OVERVIEW AND BRIEF HISTORY OF GENETIC ALGORITHMS
4-1 The Basic Concept of Genetic Algorithms 25
4-2 The History of Genetic Algorithms 25
4-3 The Advantages of Genetic Algorithms 26
4-4 Perspective on Genetic Algorithms 26
4-4-1 Reproduction 26
4-4-2 Crossover 29
4-4-3 Mutation 30
4-5 Fitness Function 31
CHAPTER 5 SIMULATION
5-1 Introduction 32
5-2 System Model 32
5-2-1 Physical Concept 32
5-2-2 Dynamical Equation 32
5-3 Random Self-Tuning for Scaling Factors 34
5-4 The Fuzzy Controller for Simulation 35
5-5 The Result for Simulation 36
5-5-1 Simulation 1 38
5-5-2 Simulation 2 39
5-5-3 Simulation 3 40
5-5-4 Simulation 4 41
5-5-5 Simulation 5 42
CHAPTER 6 EXPERIMENTAL RESULTS AND DISCUSSIONS
6-1 The Seesaw Mechanism ( The Unstable Plant ) 43
6-1-1 The Concept of Seesaw Fundamentals 43
6-1-2 The Practical Hardware 44
6-2 The Design of the Fuzzy Controller 46
6-2-1 The System State Variables 46
6-2-2 The Rules of the Conventional Fuzzy Controller 47
6-2-3 The Fuzzy Set Definitions of the Fuzzy Variables 51
6-2-4 The Fuzzy Reasoning Method 52
6-2-5 The Defuzzification Algorithm 52
6-3 The Design of the HFS 52
6-3-1 The Structure of HFS 52
6-3-2 The Rules of the Conventional Fuzzy Controller 53
6-4 The Self-Tuning by GA 54
6-4-1 The Performance-Oriented Objective Function for GA54
6-4-2 The Self-Tuning for the Rule Base 55
6-4-3 The Self-Tuning for the Membership Functions 55
6-5 Experimental Results 56
6-6 Matlab Control in Direct 56
CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS 58
REFERENCES 59

                                

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