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博碩士論文 etd-0205115-151732 詳細資訊
Title page for etd-0205115-151732
論文名稱
Title
以比例控制器與補償器實作基於圖像的視覺伺服
Image Based Visual Servoing Using Proportional Controller with Compensator
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
59
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
2015-02-16
繳交日期
Date of Submission
2015-03-05
關鍵字
Keywords
模糊小腦模型、機器手臂、近似函數、視覺伺服、加強式學習
robot Manipulator, CMAC, function approximation, visual seroing, Reinforcement learning
統計
Statistics
本論文已被瀏覽 5647 次,被下載 295
The thesis/dissertation has been browsed 5647 times, has been downloaded 295 times.
中文摘要
本篇論文主要於設計一個比例控制器利用基於Takagi–Sugeno模糊骨架的模糊小腦模型和一個補償器應用於視覺伺服,利用所提出的控制器在離線時學習模仿基於圖像的視覺伺服的控制器(IBVS),學習完成後,在實際進行視覺伺服時能有效的減少運算時間,降低運算的複雜度,並設計一補償器,在進行視覺伺服時線上的即時學習與微調控制器,補償於所設計之控制器因系統或環境不確定性造成的控制錯誤,使在追蹤目標時能更穩定且迅速,模擬結果顯示利用比例控制器與補償器能夠有效且好的追蹤到目標,本論文上的模擬實驗利用Webots這套軟體進行實驗。
Abstract
The main objective is to design a proportional controller of a robot manipulator using the fuzzy cerebellar model articulation controller based on Takagi–Sugeno (T–S) framework with a compensator. The controller and compensator apply in visual servoing, including system identification of image and kinematic Jacobians. The proposed approach is basically as a function of the visual error and extent from the error with respect to desire visual feature. This approach leads to enormous reduction on computational expense compared to the image-based approaches of model inverse kinematics. The design of the controller architecture will make it possible to implement in general case. Proportional control variable are learned offline with the help of FCMAC-T-S model, and online compensator scheme has been proposed for adapting possible uncertainties in the unknown system and environment. Fine-tuned scheme in the proposed compensator can compensate the controller output during the period of visual servoing, improving its stability while tracking the target. Stimulation results have shown that visual servoing for tracking static target can be achieved using the proposed controller with stochastic reinforcement learning compensator. The simulation of eye-in-hand configuration is implemented in Webots 7.0.3.
目次 Table of Contents
摘要 I
ABSTRACT II
LIST OF FIGURE IV
SUMMARY OF NOTATION VI
I. INTRODUCTION 1
1.1 MOTIVATION AND OBJECTIVE 1
1.2 ORGANIZATION OF THESIS 3
II. THEORETICAL BACKGROUND 4
2.1 IMAGE BASED VISUAL SERVOING 4
2.2 REINFORCEMENT LEARNING 8
2.3 ADAPTIVE HEURISTIC CRITIC 10
2.4 STOCHASTIC REINFORCEMENT LEARNING 11
2.5 CEREBELLAR MODEL ARTICULATION CONTROLLER 17
III. CONTROLLER DESIGN 21
3.1 FCMAC-T-S MODEL 23
3.2 STOCHASTIC REINFORCEMENT LEARNING COMPENSATOR 28
4.1 EXPERIMENTAL SETUP 33
4.2 VISUAL SERVOING FOR A STATIC TARGET 37
V. CONCLUSION 46
5.1 SUMMARY 46
5.2 FUTURE WORK 48
REFERENCES 49
參考文獻 References
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