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研究生:賀孝淇
研究生(外文):Hsiao-Chi Ho
論文名稱:利用非線性控制使四旋翼飛行器達成軌跡的追蹤
論文名稱(外文):Nonlinear Trajectory Tracking Control of Quadrotor Unmanned Aerial Vehicle
指導教授:連豊力
口試委員:簡忠漢李後燦
口試日期:2015-07-31
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:176
中文關鍵詞:立體攝影機三維座標重建四旋翼飛行器步階回歸適應控制
外文關鍵詞:RBG-D sensorthree-dimension coordinate reconstructionquadrotor UAVbacksteppingadaptive control
相關次數:
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隨著無人飛行器逐漸應用廣泛,設計飛行器的控制系統也漸漸變成熱門的研究議題。本篇以設計四旋翼的控制器為主題,首先建立物理動態模型,根據此模型設計控制器,其主要是由兩個控制器組成: 基準控制器及適應控制器。以基準控制器為例,基準控制器是以步階回歸來設計,作為軌跡追蹤的主要控制器 ; 而適應控制器則是根據模型參考適應控制,用來增加系統的強健性,並提供更好的穩態追蹤效果。為了測試所設計的控制器,我們提供了模擬及實際四旋翼飛行器的室內飛行數據來佐證其可性度。在取得飛行器空間位置上,我們使用其自身感測器(加速度計、陀螺儀)來做換算,並使用立體攝影機(Kinect)來得到更完整的環境狀態。利用Kinect相機所提供的深度資訊,根據影像與感測器相對關係獲得自身三維空間位置以獲得四旋翼的空間位置資訊。

As the unmanned aerial vehicles have been increasingly popular, designing a control scheme for unmanned aerial vehicle becomes a popular issues. In this thesis, the dynamic model of quadrotor is first derived. Based on the model, a controller which is a combination of baseline and adaptive controllers is presented for accomplishing the desired tasks. The baseline controller based on backstepping technique is the main controller for trajectory tracking; the adaptive controller is found to increase robustness to steady-state error using Model Reference Adaptive Controller (MRAC). Then, simulations and flight experiments are given for testing the robustness and validity of the controller. In experiments, the position of quadrotor is obtained by the external motion sensor and IMU including gyroscope and accelerometer. The external motion sensor is constructed by RGB-D sensor, Microsoft Kinect. Using the depth information from Kinect, the 3D position of quadrotor could be obtained based on the projection relation of camera.

摘要 i
ABSTRACT ii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES xii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Formulation 2
1.3 Contribution 3
1.4 Organization of the Thesis 4
Chapter 2 Background and Literature Survey 5
2.1 Applications of Quadrotor UAV 5
2.2 Control Design for Quadrotor UAV 9
Chapter 3 RBG-D Sensors for State Estimation 12
3.1 CAMSHIFT (Continuously Adaptive Mean Shift) 12
3.1.1 Color Probability Distribution and Back
Projection 14
3.1.2 Tracking Target 15
3.2 3D Position Reconstruction 18
3.2.1 Pinhole Model 18
3.2.2 Coordinate Transformation 19
Chapter 4 Controller Design for a Quadrotor UAV 22
4.1 Quadrotor Dynamic Model 23
4.2 AR.Drone Dynamic Model 27
4.3 Controller Design 29
4.3.1 Baseline Controller 29
4.3.2 Adaptive Controller 34
Chapter 5 Simulations and Experiment Results 41
5.1 Hardware Platforms 41
5.1.1 Quadrotor UAV 42
5.1.2 RGB-D Sensor 44
5.2 Accuracy Analysis of RGB-D Sensor Kinect 47
5.2.1 Experimental Setups 49
5.2.2 Accuracy of Kinect Sensor 51
5.3 Simulations of the Controller 62
5.3.1 Simulation results by quadrotor UAV model 62
5.3.2 Simulation results by AR.Drone UAV model 108
5.4 Flight Experiments 142
5.4.1 Experimental Setup 142
5.4.2 Experimental Results 144
Chapter 6 Conclusion and Future Works 163
6.1 Conclusion 163
6.2 Future Works 164
APPENDIX A 165
REFERENCES 172


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