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博碩士論文 etd-0712100-123711 詳細資訊
Title page for etd-0712100-123711
論文名稱
Title
基因演算法於傳動機構之系統鑑別
System Identification for Transmission Mechanism by Using Genetic Algorithms
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
97
研究生
Author
指導教授
Advisor
召集委員
Convenor

口試委員
Advisory Committee
口試日期
Date of Exam
2000-06-30
繳交日期
Date of Submission
2000-07-12
關鍵字
Keywords
直流伺服馬達、傳動系統、系統鑑別、最小變異數控制器、諧合式齒輪、PID 控制器、基因演算法
Genetic algorithm, PID controller, DC servomotor, System identification, Harmonic drive, Transmission system, Minimum variance controller
統計
Statistics
本論文已被瀏覽 5781 次,被下載 2263
The thesis/dissertation has been browsed 5781 times, has been downloaded 2263 times.
中文摘要
本論文主要是利用基因演算法來進行傳動系統的系統鑑別。為了使參數在搜尋的過程中能夠快速收斂且具有較佳的精度,我們考慮在傳統的基因演算法架構中加入一些策略;如菁英政策、G-bit 政策、適合度調整、基因毀滅等等。為了和傳統的基因演算法(Genetic Algorithm)有所區別,本論文將改良過後的基因演算法命名為MGA(Modified Genetic Algorithm)。此外,為了証明MGA在參數鑑別上的優越性,本論文比較利用GA、LMS (Least mean-squares) 及MGA 三種方法來鑑別ARMAX模型的系統參數,結果証明在系統受到干擾的情況下,利用MGA所鑑別出的系統參數較為準確。在收斂速度方面,也較傳統的基因演算法有大幅的改善。接著我們將MGA應用到傳動系統的參數鑑別上。首先,針對直流伺服馬達推導輸入電壓與輸出角速度的關系,再由輸入、輸出資料及MGA來估測出系統的參數。接著利用相同的方法我們也可以鑑別出整個傳動系統的參數。最後,為了確認所估測出傳動系統參數的準確性,我們利用Minimum Variance Controller (MVC) 來追尋所規畫的速度軌跡並和傳統的數位PID控制器比較,由實驗結果得知,利用MGA所估測出的系統參數具有令人滿意的精度。
Abstract
In this study, the use of modified genetic algorithms (MGA) in the parameterization of the Transmission Mechanisms is facilitated. The new algorithm is proposed from the genetic algorithm with some additional strategies, and yields a faster convergence and a more accurate search. Firstly, this near-optimum search technique, MGA-based ID method, is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to compare with the LMS (Least mean-squares) method and GA method. Then, this proposed algorithm is applied to the identification of the Transmission Mechanisms of DC motor. The parameters of the friction force and DC motor are estimated in a single identification experiment. It is also shown that this technique is capable of identifying the whole transmission system. Finally, the Minimum Variance Controller (MVC) is taken to track the desired speed trajectory and then a comparison to the conventional digital PID controller is shown. Experiment results are included to demonstrate the excellent performance of the MVC.

目次 Table of Contents
Captions of Symbols iii
Captions of Tables vii
Captions of Figures viii

Chapter 1 Introduction 1
Chapter 2 Concerning about Genetic Algorithms 4
2.1 History of Genetic Algorithms 4
2.2 Background 4
3.3 Simple Genetic Algorithm (SGA) 5
3.4 Modifications to Simple
Genetic Algorithm 9
Chapter 3 MGA-based System Identification
method 16
3.1 Problem Statement 16
3.2 Algorithm Statement 17
3.3 Simulation 22
Chapter 4 System Identification of the DC motor 30
4.1 Curve approximation ID method 30
4.2 Gray-box ID method 33
4.3 MGA-based ID method 36
Chapter 5 Identification of DC Motor with
Harmonic Drive 46
5.1 About Harmonic Drive 46
5.2 MGA-based ID method 48
Chapter 6 Controller Design and Experiments 56
Chapter 7 Conclusions and Recommendations 65
7.1 Conclusion 65
7.2 Suggestions for Future Research 66
Appendix A Reviewing for System Identification 67
A.1 Basic Theory of the System
Identification 67
A.1.1 Characteristics of the Input
Signal 68
A.1.2 Variants of Model Descriptions
70
A.1.3 Optimization Methods 72
A.1.4 Validation Methods 73
A.2 The System Identification
procedure 75
Appendix B Basic mathematical structure of the
MVC 77
Appendix C Specifications and Laboratory
Apparatus 80

Appendix D Matlab code of the MGA-based ID method
83

Reference 91



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