摘要
本論文使用非二進位制編碼及解碼政策、菁英政策、漸增突變率政策、人口汰新政策來改良簡單型因演算法，使得改良後的基因演算法在大範圍的參數搜尋時,能減低因過早收斂而落入局部最佳解的可能性，並且明顯提升搜尋到最佳參數的機會。
本研究以加速規為回授感測器，搭配致動器及利用改良型基因演算法所設計出的模糊PID控制器，對線性馬達在高速精密定位時所造成的機台振動作主動式振動抑制。且經由電腦模擬及實驗結果可以看出，改良型的基因演算法所設計出的模糊PID控制器能減少系統安定時間，而有效的抑制線性馬達在快速精密定位時所造成的平台振動，使得激振系統快速到達穩定。

Abstract
We use the non-binary coding ,elitist strategy, increasing mutation rate, extinction, and immigration strategy to improve the simple genetic algorithms in this study. We expect that the search technique can avoid falling into the local optimum due to the premature convergence, and purse the chance that finding the near-optimal parameters in the larger searching space could be obviously increased.
The accelerometer is then taken as the feedback sensor for output measurement, and the designed actuator and the PID fuzzy logic controller (PIDFLC) is implemented to actively suppress the vibration of the supporting mechanism that is due to the excitation effect of the high-speed and precision positioning action of the linear motor. From the computer simulations and the experimental results, it is obvious that the near-optimal PIDFLC controller designed by modified genetic algorithms can improve the effect of the vibration suppression; the settling time is also decrease. For the vibration suppressions of high-speed precision positioning problems, the vibrating supporting mechanism can quickly be stabilized.

Contents Ⅰ
List of Symbols Ⅳ
List of Figures Ⅵ
List of Tables Ⅸ
Chinese Abstract Ⅹ
English Abstract ⅩⅠ

Chapter 1. Introduction and Papers Review 1
1.1Research Motivation and Goal 1
1.2 Papers Review 1
1.2.1 Building of System model 1
1.2.2 Vibration Control and Actuator 2
1.3 Genetic Algorithms and Fuzzy logic Controller 3
1.4 Research Results and Contributions 5
1.5 Thesis Structure 5

Chapter 2. Concerning about Genetic Algorithms 6
2.1 Brief History of Genetic Algorithms 6
2.2 Foundations of Genetic Algorithms 7
2.3 Simple Genetic Algorithms (SGA) 9
2.4 Modifications to Simple Genetic Algorithms 13
2.4.1 Encoded and Decoded Processes 14
2.4.2 Fitness Function Definition 14
2.4.3 Fitness Scaling 15
2.4.4 Reproduction Operator 16
2.4.5 Crossover Operator 17
2.4.6 Mutation Operator 18
2.4.7 Elitist Strategy 19
2.4.8 Extinction and Immigration Strategy 20
2.4.9 The Structure of Modified Genetic Algorithms 23

Chapter 3. Concerning about Fuzzy Systems and Control 25
3.1 Introduction of Fuzzy Systems and Control 25
3.2 Fuzzy sets and Membership Functions 27
3.3 Simplified Fuzzy Reasoning Method 30
3.4 Hybrid Reduced Rule Fuzzy PID Controller 33

Chapter 4. Dynamic Model Building and Hybrid Reduced Rule Fuzzy
PID Controller Design 37
4.1 Analysis of Exciting System 37
4.2 Exciting System and Actuator Model 40
4.2.1 Identification of Equivalent System Mass 40
4.2.2 Identification of Equivalent Elastic Coefficient 40
4.2.3 Identification of Equivalent Damping Coefficient 43
4.2.4 Actuator Model 46
4.3 Hybrid Reduced Rule Fuzzy PID Controller Design 48
4.3.1 About Exciting Force 48
4.3.2 Modification of Hybrid Reduced Rule Fuzzy PID
Controller 52
4.3.3 Suggestions about Simultaneous Design of Membership
Functions and Rule Bases for PIDFLC Using GAs 54
4.3.4 Design Steps and Simulation for PIDFLC by
Simultaneous design of Membership Functions and
Rule Bases Using GAs 56

Chapter 5. Experiments and Results 62
5.1 Experiment Steps 62
5.2 Experiment Results 64
Chapter 6. Conclusions and Recommendations 69
6.1 Conclusions 70
6.2 Recommendations 70
References 71

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