模糊控制與灰色預測於機械手臂視窗化之控制應用

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姓名

陳建宏(Jian-Hung Chen ) 查詢紙本館藏

畢業系所

電機工程研究所

論文名稱

模糊控制與灰色預測於機械手臂視窗化之控制應用
(Fuzzy Controller and Grey Prediction Model Applied to the)

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摘要(中)

本論文將探討模糊控制及灰色預測應用於三軸機械手臂的運動控制。最近，模糊控制器已成新興的潮流，雖然其設計時很方便，但對機械手臂的設計，往往會有超越量的情形發生及較差的暫態和穩態響應。因此，針對這個問題，本論文將採取灰色預測當做前饋式補償器，如此設計的模糊控制器，當超越量產生之前，便以力量之補償方式降下來，可預防超越量的產生，並且於上升時間也有不錯的暫態響應及較小的穩態誤差，並且使用最佳化之演算法－基因演算法去調適灰色預測內重要參數ak，使整個系統設計更加完善。再進一步討論，將設計比例-微分-積分控制器，並且與提出的方法做比較及分析。此外，本論文使用模糊速度控制器去控制手臂的等速運動及防止干擾(如負載量的變化)。最後，發現在大部分實驗中，機械手臂均可達到預期目標，而且藉由視窗化的軟體，將所有實驗及設計均視窗化，以求達到良好的人機使用介面。本論文將在最後探討小部分結果不甚理想的原因，並提出未來可以改進的方向。

摘要(英)

In this research, we first provide a fuzzy controller with the grey prediction feedforward compensator to replace the conventional position controller. The grey model has some efficacy in forecasting the overshoot of the response. In order to obtain the accurate prediction, the constant parameter ak of grey model needs to be optimal. Hence, we use a SGA (Simple Genetic Algorithm) to obtain the proper constant ak. Next, the fuzzy controller is presented to solve the speed control problem. The fuzzy speed controller not only maintains the constant speed but also prevents the disturbance. In the implementation process, the design is completed under the windows software. Therefore, it is easy to fix the parameters rapidly and get the fixed sampling time. Then, the discussion of the experimental results is given. Finally, it is found that the performance of both the position control and the speed control of a three-axis robot arm is satisfactory.

關鍵字(中)

★ 模糊控制器
★ 機械手臂
★ 灰色預測
★ 簡易遺傳演算法
★ 視窗

關鍵字(英)

★ Fuzzy controller
★ Grey prediction
★ Robot arm
★ SGA
★ Windows

論文目次

List of Contents
Content Page
Abstract
List of Contents I
List of Figures III
List of Tables V
Chapter 1. Introduction
1.1 Background 1
1.2 Motivation 2
1.3 Organization 3
Chapter 2. Problem Description and Proposed Method
2.1 Problem Description 4
2.2 Fuzzy Controller 5
2.3 Grey Prediction 7
2.4 Simple Genetic Algorithm 10
2.5 Proposed Method 13
Chapter 3. The Structure of the Hardware and Software
3.1 The hardware of robot arm 16
3.2 The supersonic sensor 16
3.3 Interface 18
3.3.1 Encoder/DAC interface 18
3.3.2 Analog Input/Output interface 19
3.3.3 Digital Input/Output interface 20
3.4 The Robot Arm 21
3.5 The Software 21
Chapter 4. Experimental Results and Discussions
4.1 Hardware Simulations 27
4.2 Experiment 1: the Position Control of the sita-axis 27
4.2.1 PID Controller 29
4.2.2 Proposed Method (Grey Prediction Feedforward
Compensator) 32
4.2.3 Discussions Conclusion of the experiment 1 43
4.3 Experiment 2: the Speed Control of the sita-axis 43
4.3.1 Fuzzy speed controller with the extension of
R-axis 45
4.3.2 Fuzzy speed controller with the load variation of
R-axis 48
4.3.3 Discussions Conclusion of the experiment 2 49
Chapter 5. Conclusions and Recommendations
5.1 Conclusions 51
5.2 Recommendations 52
References 54
Appendix A 56

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指導教授

鍾鴻源(Hung-yuan Chung)

審核日期

2001-6-28

推文