臺灣博碩士論文加值系統

本篇論文提出了一個自我產生的類神經模糊控制器，首先控制器的功能能即時控制永磁式同步馬達的轉速，其目的為了追蹤週期性的步階及正弦參考輸入訊號。而控制器的神經元具有自我建構的能力，神經元建構後會做線上的參數學習。參數學習是在輸入神經元的部份，學習的方法是使用監督式梯度下降法。最後以幾個模擬結果，來驗證我們所提出的自我產生類神經模糊控制器，在馬達參數的變化或加入外部的負載干擾下，具有快速的學習能力及準確的追蹤性能。

This thesis presents the design and implementation of an Automatic Generation Fuzzy Neural Network (ADFNN) controller suitable for real-time control of the speed control of the permanent-magnet synchronous motor (PMSM) to track periodic step and sinusoidal reference inputs. The structure and parameter learning are done automatic and online. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient decent method using a delta law. Several simulation results are provided to demonstrate fast learning rate and accurate tracking performance of the proposed ADFNN control stratagem under the occurrence of parameter variations and external disturbance.

CHINESE ABSTRACT i
ENGLISH ABSTRACT ii
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
CHAPTER
1. INTRODUCTION 1
2. THE FUNDAMENTAL OF FUZZY SYSTEM AND NEURA NETWORK AND FUZZY NEURAL NETWORK 5
2.1 The Basic Concept of Fuzzy System 5
2.1.1 Fuzzification Interface 7
2.1.2 Knowledge Base 7
2.1.3 Inference Engine 8
2.1.4 Defuzzification Interface 9
2.2 The Basic Concept of Neural Network 10
2.3 The Basic Concept of Fuzzy Neural Network 16

3. AUTOMATIC GENERATION FUZZY NEURAL NETWORK 20
3.1 Control of the Rotor Speed of a Permanent-Magnet Synchronous
Motor Drive System 20
3.2 Structure of the Automatic Generation Fuzzy Neural Network 24
3.3 Online Learning Algorithms for AGFNN 27
3.3.1 Structure Learning Phase 28
3.3.2 Parameter Learning Phase 32
4. SIMULATION RESULTS 37
4.1 Simulation Results for PI Speed Controller and Self-Constructing
Fuzzy Neural Speed Controller 37
4.2 Simulation Results for AGFNN Speed Controller 56
5. CONCLUSION 64
REFERENCES 65

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