臺灣博碩士論文加值系統

在現實生活，大多數具物理意義的系統屬於非線性系統，利用回授線性化控制時，系統不確定性及干擾會影響到控制器的可行性，並使閉迴路系統難以達到預期性能，為了解決此類不確定性的問題，本文提出以Adaptive-Network-Based Fuzzy Inference System (ANFIS)為基礎的H_∞模糊滑動控制器來達到此預設性能。
在本文中，綜合控制器是由兩個部分組成，第一部分是以回授線性化為基礎的滑動控制器，其控制增益及觀測增益是由H_∞ 標準控制理論所求出，另一部分是以模糊控制為基礎的控制器，其歸屬函數係由ANFIS最佳化學習得到。在綜合控制器作用下，系統誤差會隨著第一部分控制器逼近滑動面，為確保追蹤性能以及預設性能，第二部分控制器可消除系統不確定性誤差。為確保本文之綜合控制器的有效性，本研究以里阿普諾夫穩定理論來證明閉迴路系統之穩定性，並以不等式來探討不確定性的最大容許範圍。
最後，本文以機械手臂來進行電腦模擬，追蹤預設路徑。從模擬結果可以看出，即使含有干擾及不確定項，系統仍保有良好的追蹤性能，依此可驗證控制器的可行性及強健性。

In practical, most of physical systems are nonlinear, and the feedback linearization method may be utilized to control the system. However, the system uncertainties and disturbances will affect the feasibility of the controller and make the closed-loop system hard to match the desired performance. To solve this problem, in this thesis, the ANFIS-based H_∞ Fuzzy Sliding Mode Controller is derived to form the proposed robust controller and to meet the pre-specified specifications.
The proposed controller is composed of two control components. One component is the feedback-linearization based sliding mode controller, in which the control gains and the observer gains are optimally chosen from the standard H_∞ control problem. The other component is the fuzzy-based controller, whose membership functions are optimally tuned by the Adaptive-Network Based Fuzzy Inference System (ANFIS). With the proposed controller, the system tracking errors will be forced to the sliding surface by the first component. To ensure the achievement of tracking performance and desired specifications, the errors resulted from system uncertainties will be eliminated by the second component. The closed-loop system stability of the proposed controller is proved by the Lyapunov’s stability criterion. The largest allowable scale of uncertainties is then explored by an inequality proposed in this research.
Finally, a robot manipulator with uncertainties is simulated to prove the feasibilities of the proposed composite controller.

Table of Contents
摘要 I
Abstract II
致謝 III
List of tables V
List of figures VI
Chapter 1: Introduction 1
1.1 Motivation 1
1.2 Literature reviews 2
1.3 Thesis outline 4
Chapter 2: Sliding mode control theory 5
2.1 Introduction 5
2.2 Applied SMC theory 5
2.3 System description 7
2.4 SMC controller 10
2.5 The robustness of the SMC controller 11
Chapter3: Adaptive-Network-Based Fuzzy Inference System 15
3.1 Introduction 15
3.2Fuzzy control and neural network 15
3.2.1 Fuzzy control 15
3.2.1.1 Fuzzification interface 16
3.2.1.2 Fuzzy knowledge base 16
3.2.1.3 Decision-making logic 17
3.2.1.4 Defuzzification interface 17
3.2.2Neural network 17
3.2.2.1 Classification of neural networks 18
3.2.2.2 Operation and characteristic of neural networks 19
3.2.2.3 Back propagation neural networks 21
3.3 ANFIS 22
3.3.1 ANFIS structure 24
3.3.2 ANFIS toolbox 26
Chapter 4: H_∞ control theory 31
4.1 Preface 31
4.2 Concepts of H_∞ control theory 31
4.3 The variation approach 32
4.3.1 Augmented system 33
4.3.2 State feedback controller 35
4.3.3 State observer S_o (s) 36
4.4 Procedure of solving H_∞control problem 39
Chapter 5: H_∞ ANFIS-Based Fuzzy Sliding Mode Controller 40
5.1 System description 40
5.2 Sliding mode controller 41
5.3 Adaptive-Network-based Fuzzy Inference 44
5.3.1 Introdction of ANFIS structure 44
5.3.2 Generate fuzzy inference system 46
5.3.2.1 Loading data 47
5.3.2.2 Generating FIS structure 47
5.3.2.3 Train the FIS 47
5.3.2.4 Validating the trained FIS 48
5.4 Design of H_∞ ANFIS-based fuzzy sliding mode controller 50
5.5 Controller design step and procedure 66
Chapter 6: Simulation 68
6.1Design a fuzzy controller u_A 68
6.1.1Plant 68
6.1.2Fuzzy logic control 68
6.1.3 ANFIS-based fuzzy controller 75
6.2 System description 77
6.2.1 Reference trajectory 79
6.2.2 Design procedure 83
6.2.3 Conclusion of manipulator 104
6.3 System description 105
6.3.1 Submarine system description 105
6.3.2 Design procedure 110
Chapter 7: Conclusion 133
References 134

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