Takagi-Sugeno模糊控制系統之穩定度條件放寬與控制器設計

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研究生：	顏仲陵 Jung-Ling Yan
論文名稱：	Takagi-Sugeno模糊控制系統之穩定度條件放寬與控制器設計 Relaxed Stability Conditions and Controller Design for Takagi-Sugeno Fuzzy Control Systems
指導教授：	莊堯棠 Yau-Tarng Juang
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：	97
語文別：	英文
論文頁數：	107
中文關鍵詞：	穩定度放寬條件、T-S 模糊控制系統
外文關鍵詞：	relaxed stability conditions, T-S fuzzy control system
相關次數：	點閱：14 下載：0
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非線性控制系統的穩定度判別是基於李亞普諾夫穩定準則(Lyapunov stability criterion)，保證Takagi-Sugeno模糊控制系統穩定的充分條件，就是試圖找到一個共同正定矩陣P (common P)讓所有子系統皆滿足李亞普諾夫不等式(Lyapunov inequality)，而控制器的設計方法多採用平行分配補償器(PDC)的概念，此方法稱之為共同二次李亞普諾夫函數(common quadratic Lyapunov function - CQLF)分析法，此共同正定矩陣P可透過MATLAB的線性矩陣不等式(LMI)控制工具箱的解法所求得，然而當模糊系統的規則數過多時，可能就無法找到這個共同正定矩陣P來符合所有子系統。
為了放寬此穩定度條件的保守性，近年來許多學者提出了許多有別於尋找單一共同正定矩陣P的新方法來定義李亞普諾夫函數 (Lyapunov function)，常見的有模糊二次李亞普諾夫函數(fuzzy quadratic Lyapunov function - FQLF)分析法、模糊線積分李亞普諾夫函數(fuzzy line-integral Lyapunov function - FLILF)分析法和切換式二次李亞普諾夫函數(switching quadratic Lyapunov function - SQLF)分析法來推導Takagi-Sugeno模糊控制系統的充份穩定度條件，理論上此三種穩定度條件會比傳統的條件寬鬆許多，因為傳統的穩定準則只是此三種方法的特例。
由上述三種方法，我們可以藉由修改歸屬函數微分之絕對值上界的條件來放寬模糊二次李亞普諾夫分析法，另外藉由重建切換式Takagi-Sugeno模糊模型來放寬切換式二次李亞普諾夫函數分析法，最後則是結合模糊線積分李亞普諾夫函數分析法和切換式二次李亞普諾夫函數分析法兩大概念而推導出切換式模糊線積分李亞普諾夫函數分析法(switching fuzzy line-integral Lyapunov function - SFLILF)，並以幾個例子來證明我們所提出的穩定度放寬條件的可行性。

The stability condition of nonlinear control system is based on the Lyapunov stability criterion. That tried to find a single positive-definite matrix P (common P) to satisfy all Lyapunov inequalities. Then the sufficient stability condition of Takagi-Sugeno fuzzy control system (T-S fuzzy control system) can be guaranteed. Furthermore, the controller design is using the Parallel Distributed Compensation (PDC) concept. This analysis method is so-call common quadratic Lyapunov function (CQLF) method. We use the linear matrix inequality (LMI) Control Toolbox of MATLAB to seek for a common P. However, if the number of rules of a fuzzy system is large, the common P may not be found.
In order to relax the conservative of stability conditions, recent years many researchers have proposed several approaches different from a single common P. There have three common methods which redefine the new Lyapunov function are fuzzy quadratic Lyapunov function (FQLF) method, fuzzy line-integral Lyapunov function (FLILF) method and switching quadratic Lyapunov function (SQLF). Theoretically, these three methods are more relaxed than traditional method; because of the traditional analysis method is just a special case of these three methods.
By the above three methods, we can revise the condition that are time-derivatives of membership functions’ absolute values to relax the FQLF method. Besides, reconstruct the switching T-S fuzzy model to relax the SQLF method. Finally is unifies the FLILF method and SQLF method concept to derive the switching fuzzy line-integral Lyapunov function (SFLILF) method. The effectiveness of the proposed approach is shown through numerical examples.

Abstract (Chinese) Ⅰ
Abstract (English) Ⅱ
Acknowledgement Ⅲ
Table of Contents Ⅳ
List of Figures Ⅶ
Nomenclature Ⅸ
Acronyms Ⅹ
Chapter 1 Introduction
1 Motivations and Background 1
2 Literature Reviews 2
3 Organization 2
Chapter 2 Descriptions of Takagi-Sugeno Fuzzy Control Systems and Problem Formulations
1 Introduction 4
2 Takagi-Sugeno Fuzzy Control Systems and Its Stability Conditions 5
2.1 Takagi-Sugeno Fuzzy Model	 5
2.2 Parallel Distributed Compensation 7
2.3 Stability Conditions Based on Common Quadratic Lyapunov Function 9
3 Problem Formulations 12
4 Summary 13
Chapter 3 Relaxed Stability Conditions and Controller Design based on Fuzzy Lyapunov Quadratic Function
1 Introduction 14
2 Stability Conditions based on Fuzzy Quadratic Lyapunov Function 15
2.1 Fuzzy Quadratic Lyapunov Function 16
2.2 Stability Conditions based on Fuzzy Quadratic Lyapunov Function 16
3 Relaxed Stability Conditions based on Fuzzy Quadratic Lyapunov Function	18
4 Controller Design based on Fuzzy Quadratic Lyapunov Function 26
5 Numerical Example 30
6 Summary 43
Chapter 4 Relaxed Stability Conditions and Controller Design based on Switching Lyapunov Function
1 Introduction 44
2 Switching Takagi-Sugeno Fuzzy Control Systems and Its StabilityConditions 46
2.1 Switching Takagi-Sugeno Fuzzy Model 47
2.2 Switching Quadratic Lyapunov Function 49
2.3 Stability Conditions based on Switching Quadratic Lyapunov Function 51
3 Stability Conditions based on Fuzzy Line-Integral Lyapunov Function 52
3.1 Takagi-Sugeno Fuzzy Model via Vertex Expression 52
3.2 Fuzzy Line-Integral Lyapunov Function 55
3.3 Stability Conditions based on Fuzzy Line-Integral Lyapunov Function 57
4 Relaxed Stability Conditions based on Switching Quadratic Lyapunov Function 57
4.1 Reconstruction of Switching Takagi-Sugeno Fuzzy Model via Vertex Expression 59
4.2Relaxed Stability Conditions based on Switching Quadratic Lyapunov Function 64
5 Relaxed Stability Conditions based on Switching Fuzzy Line-Integral Lyapunov Function 67
6 Controller Design based on Switching Fuzzy Line-Integral Lyapunov Function 73
7 Numerical example 74
8 Summary 87
Chapter 5 Conclusions and Feature Work
1 Conclusions 88
2 Feature Work 88
References  89

                                

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