臺灣博碩士論文加值系統

本論文將提出一灰色模糊PID控制程序應用於船舶自動導航控制系統。灰色模糊PID控制程序包括兩個部分：灰色預測器及模糊PID控制器。灰色模糊PID控制器和模糊PID控制器之間最大的不同是，灰色模糊PID控制器是使用灰色模型來預測未來的狀態，因此，有預先控制的優點。在實際的工程問題上，我們經常面對多個目標最佳化的問題，因此，為了經由有系統的推理來搜尋最佳的控制參數，以取代耗費時間的試誤法，強健多目標最佳方法被應用在本論文中，來搜尋結合灰色預測器及模糊PID控制器兩者的最佳控制參數（也就是灰色預測器的取樣數目，模糊PID控制器的量化因子，及S形函數的常數），確保控制性能的穩定。強健多目標最佳方法結合多目標最佳化觀念及靜態田口方法，透過直交表實驗配置的幫助，並使用變異數分析的結果來量化設計參數的重要性，可獲得一Pareto最佳強健設計解集合。所完成的航向保持自動導航電腦模擬結果證實強健多目標最佳灰色模糊PID控制程序的有效性，而且顯示強健多目標最佳灰色模糊PID控制程序優於現有的模糊PID控制程序。

In this thesis, the grey-fuzzy gain scheduling (GFGS) PID control scheme is proposed for autopilot system. The GFGS PID control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling (FGS) PID controller. The difference between GFGS PID controller and FGS PID controller is that the GFGS PID controller is designed based on the future state, which is predicted by the grey model. Thus there will be the advantage of prior control. In real engineering problems, we usually face the multiple-objectives optimization problems. Therefore, in order to search for the optimal control parameters by way of systematic reasoning instead of the time-consuming trial-and-error procedure. An optimal combined method, i.e., robust multi-criteria optimization (RMCO) approach, is applied in this thesis to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling PID controller (i.e., the sample size of the grey predictor, the scaling factors of the fuzzy PID controller and factors of sigmoid membership functions) for ensuring both stability and control performances. RMCO approach integrates multi-objective optimization concepts with statistical Taguchi method and obtains a Pareto-optimal robust design solution set with the aid of design of experiment set-ups, which uses analysis of variance (ANOVA) results to quantify relative significance of design factors. Computer simulations of the course-keeping autopilot are performed to verify the effectiveness of the RMCO-GFGS PID control scheme and to show that the GFGS PID control scheme is superior to the existing FGS PID control scheme.

CONTENTS

摘要i
ABSTRACTii
ACKNOWLEDGEMENTSiii
CONTENTSiv
LIST OF TABLESvi
LIST OF FIGURESvii
NOMENCLATUREviii
CHAPTER 1 INTRODUCTION1
1.1 Motivation1
1.2 Literature Survey2
1.3 Brief Sketch of the Contents3
CHAPTER 2 SYSTEM DESCRIPTIONS5
2.1 Ship Course-Keeping Handling Control5
2.2 Robust Multiple-Objectives Optimization Grey-Fuzzy Gain Scheduling PID Controller8
CHAPTER 3 ROBUST MULTI-CRITERIA OPTIMIZATION APPROACH19
3.1 RMCO Approach Procedure19
3.2 Orthogonal Arrays (OAs)21
3.3 Signal-to-Noise Ratios (SNR)22
3.4 Analysis of Variance (ANOVA)24
CHAPTER 4 FUZZY CONTROL THEORY26
4.1 Fuzzifier28
4.2 Fuzzy Knowledge Base29
4.3 Inference Engine30
4.4 Defuzzifier31
CHAPTER 5 GREY SYSTEM THEORY33
5.1 The Grey Model - GM (1,1) Model34
5.2 Grey Prediction Control37
CHAPTER 6 PARAMETER DESIGN AND COMPUTER SIMULATIONS40
CHAPTER 7 CONCLUSIONS AND DISCUSSIONS50
REFERENCE51

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