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、人口汰新政策之改良簡單型基因演算法，此改良後之基因演算法可在大範圍的參數搜尋時,減低因過早收斂而落入局部最佳解的可能性，並且提升搜尋到最佳參數的機會。
本研究中使用之液壓成形實驗機包含有油壓馬達驅動源及雙液壓缸致動器，利用內壓力與軸向力來達到管材成形的目的。實驗時利用改良型基因演算法所設計出的模糊PID控制器來控制比例方向閥及壓力閥，以使其負載路徑遵循預設之最佳成形進給-壓力曲線。由實驗結果可看出，此控制方式可達到良好的成形結果，並有效避免因負載的不確定性及液壓系統的非線性因素所造成的系統不穩定。

In this study, the non-binary coding, elitist strategy, increasing mutation rate, extinction, and immigration strategy are used to improve the simple genetic algorithms. The improved search technique can reduce the possibility of falling into the local optimum due to the premature convergence in a large searching space, and increase the chance of finding out the near-optimal parameters.
The hydraulic forming machine used in this thesis, includes a power source of a hydraulic motor and a actuator of two hydraulic cylinders. Both the internal pressure and axial force are controlled to hydroform the tubes into the shapes we want.
The PID fuzzy logic controller is implemented to control the proportional direction valve and pressure reducing valve of this dual-cylinder electro-hydraulic system so that the loading path can follow the optimal forming curve of axial-feeding and pressure prescribed. From the experimental results, it is clear that the near-optimal PIDFLC controller designed via modified genetic algorithms can make the loading path follow the prescribed curve, and effective for reducing system uncertainty caused by the varying loads and system unstability resulting from the nonlinear characteristics of the hydraulic system.

Contents I
Chinese Abstract Ⅲ
English Abstract Ⅳ

Chapter 1. Introduction and Paper Survey 1
1.1 Motivation and Goal 2
1.2 Paper Survey 3
1.2.1 Proportional Valves 3
1.2.2 Electro-Hydraulic System 4
1.2.3 Genetic Algorithms and Fuzzy Logic Controller 5
1.3 Thesis Structure 5

Chapter 2. Modeling of Tube Hydroforming System 7
2.1 Proportional Control Valves 8
2.2 Dynamic Model of System 10

Chapter 3. Genetic Algorithms 16
3.1 Introduction to Genetic Algorithms 16
3.2 General Structure of Genetic Algorithms 17
3.2.1 Data Structure 17
3.2.2 Encoded and Decoded Process 18
3.2.3 Fitness Function Definition 19
3.2.4 Fitness Scaling 19
3.3 Simple Genetic Algorithms 22
3.4 Modifications to Genetic Algorithms 25
3.4.1 Reproduction Operator 26
3.4.2 Crossover Operator 26
3.4.3 Mutation Operator 27
3.4.4 Elitist Strategy 29
3.4.5 Extinction and Immigration Strategy 30
3.4.6 The Structure of Modified Genetic Algorithms 32

Chapter 4. Fuzzy System 34
4.1 Introduction of Fuzzy Control System 34
4.2 Fuzzy Sets and Membership Function 35
4.3 Fuzzy Controllers 37
4.3.1 Simplified Fuzzy Reasoning Method 40
4.4 Hybrid Reduced Rule Fuzzy PID like Controller 43

Chapter 5. Hybrid Reduced Rule Fuzzy PID Controller Design 47
5.1 Suggestions about Simultaneous Design of Membership Functions and Rule Bases using GAs 47
5.2 Design Steps and Simulation for PIDFLC using GAs 49

Chapter 6. Experiments and Results 55
6.1 Experiment Instruments 55
6.2 Experiment steps 65
6.2.1 Performance of Instruments 65
6.2.2 System Response 70
6.2.2 Experiment steps 74
6.3 Experiment Results 74

Chapter 7. Conclusions and Recommendations 77
7.1 Conclusions 77
7.2 Recommendations 78

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