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系統識別號 U0026-2908201716594300
論文名稱(中文) H∞控制器結合人工勢場法於自主式水下載具避碰與導航之研究
論文名稱(英文) Research on Autonomous Underwater Vehicle Collision Avoidance and Navigation Based on H∞ Controller and Artificial Potential Field Method
校院名稱 成功大學
系所名稱(中) 系統及船舶機電工程學系
系所名稱(英) Department of Systems and Naval Mechatronic Engineering
學年度 105
學期 2
出版年 106
研究生(中文) 王舜民
研究生(英文) Shun-Min Wang
學號 P18971030
學位類別 博士
語文別 英文
論文頁數 135頁
口試委員 指導教授-方銘川
指導教授-黃正能
口試委員-方志中
口試委員-郭振華
口試委員-王兆璋
口試委員-陳信宏
口試委員-林宇銜
中文關鍵字 自主式水下載具  H∞控制器  人工勢場法  避碰與導航 
英文關鍵字 autonomous underwater vehicle  H∞ controller  artificial potential field method  obstacle avoidance and navigation 
學科別分類
中文摘要 本論文提出H∞控制器結合人工勢場法(artificial potential field method, APFM)以解決自主式水下載具(autonomous underwater vehicle, AUV)避碰與導航問題,我們應用人工勢場法設計了深度、高度及航向等三種控制演算法,以實現AUV在未知三維靜態環境之障礙物避碰與導航控制;在深度控制演算法中,將載具的安全高度納入考量,以避免AUV碰撞到海底或垂向障礙物;在高度控制演算法中,將載具的最大安全深度納入考量,以避免AUV過度跟隨海底地形,而超出載具可承受最大壓力的安全深度;而航向控制演算法是基於改良式人工勢場法,除了解決人工勢場法局部極小值問題之外,並結合障礙物邊緣跟隨法(obstacle boundary following method, OBFM)解決了AUV落入U型陷阱及重覆路徑徘徊等問題。
在模擬分析上,以自行開發的自主式水下載具NCKU-AUV作為研究對象,經由平面運動機構(planar motion mechanism, PMM)實驗取得了載具的流體動力係數做為系統模擬參數,建立數學模型設計了H∞控制器,並以迴路整型方法調整權重函數,獲取最佳化控制器,經由模擬結果得知該控制器具有強健性及抗干擾性,滿足系統性能要求及穩定性;另外,亦對於深度、高度及航向等控制演算法進行模擬,在不同深淺的海底地形及障礙物狀況假設下,經模擬實驗結果得知,所提出的避碰控制演算法的有效性,正確地引導AUV避開障礙物安全地潛航,且在H∞控制器的執行下準確地導航到達指定目標。
在實測方面,由於NCKU-AUV載具上安裝有都卜勒速度儀(DVL)、深度計、高度計及慣性量測單元(IMU)等感測器,可量測AUV的速度、深度、高度及姿態角度等資訊,尤其在載具的前端架設5支測距聲納(高度計)做為水平及垂直方向障礙物偵測,可量測載具與障礙物之間的距離;AUV於國立成功大學的拖航水槽中進行實測,在水槽底放置有障礙物,並將所圍成的水槽壁作為U型障礙物。我們分別測試了不同的避碰導航任務,如定深控制、定高控制、定航向控制等,實驗結果得知AUV成功地完成任務。依水槽測試所獲得的初步結果,驗證本論文所提出H∞控制器結合人工勢場法的可行性與有效性,未來將再進一步進行實海實測以證明其實用性。
英文摘要 This dissertation proposes integrating an H∞ controller with an artificial potential field method (APFM) to solve collision avoidance and navigation issues in an autonomous underwater vehicle (AUV). We applied APFM in designing three types of control algorithms—altitude, depth, and heading; the AUV used the proposed control algorithms to navigate in unknown three-dimensional static environment and avoid collisions with obstacles. The depth-control algorithm involved a safe altitude above the seafloor to prevent the AUV from colliding with the seafloor or vertical obstacles. The altitude-control algorithm involved a maximum safe depth below the surface to prevent the AUV from following terrain beyond the maximum pressure of vessel strength. The heading-control algorithm was based on an improved APFM to solve the local minimum problem; it combined APFM with an obstacle boundary following method (OBFM) to solve the problems of the AUV falling into the U-shaped trap and repetition path hovering.
For simulation analysis, we modeled a self-developed National Cheng Kung University Autonomous Underwater Vehicle (NCKU-AUV) as a device under test. Using a planar motion mechanism (PMM) test, we obtained the hydrodynamic force coefficient of the vehicle. We applied the system simulation parameters for building the mathematical model used to design the H∞ controller. We used the loop shaping method to adjust the weight function, obtaining the optimal controller. The simulation results showed that the controller had robustness and anti-interference properties, met system performance requirements, and provided stability. Additionally, we tested the control algorithms for altitude, depth, and heading, simulating seafloor with different depths, different terrain, and various types of obstacles; the simulation results showed that the proposed collision avoidance algorithm was effective and guided the NCKU-AUV to avoid the underwater obstacles safely and correctly. Using the simulated H∞ controller, the simulated NCKU-AUV was able to accurately navigate to the appointed target.
For practical testing, physical sensors including a Doppler velocity log, depth gauge, altimeter, and inertia measurement unit were installed on the physical NCKU-AUV to measure its velocity, altitude, depth, and attitude angle. In particular, five sonar sensors were installed at the bow end of the vehicle to detect horizontal and vertical obstacles; these sonar sensors were able to measure the distances between the vehicle and obstacles. AUV trials were carried out in the towing tank at NCKU. Obstacles were placed at the bottom of the towing tank, and the walls of the tank acted as a horizontal U-shaped obstacle. All control tests (fixed altitude, fixed depth, and navigation) were conducted and completed successfully. Preliminary results of the towing tank test validated the feasibility and effectiveness of the proposed H∞ controller with APFM; real sea testing could be conducted in future to prove the practicality of this system.
論文目次 中文摘要 I
Abstract III
Acknowledgements V
Table of Contents VII
List of Tables X
List of Figures XI
Nomenclature XIV
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Literature Review 4
1.3 Research Purpose 6
1.4 NCKU-AUV Description 6
1.4.1 Hardware Architecture 8
1.4.2 Software Architecture 10
1.5 Organization of Dissertation 12
Chapter 2 Research Methods 13
2.1 Mathematical Model 13
2.1.1 Coordinate System 13
2.1.2 AUV Kinematics 14
2.1.3 AUV Dynamics 15
2.2 Artificial Potential Field Method (APFM) 25
2.2.1 Basic principle of APFM 25
2.2.2 Local Minima Problem 27
2.3 Improved Artificial Potential Field Method (IAPFM) 28
2.4 H∞ Control Theory 30
2.4.1 Control Theory 30
2.4.2 Loop Shaping 35
2.4.3 Uncertainties 39
2.4.4 Sensitivity Matrix 45
2.4.5 Selecting Weighting Functions 47
2.4.6 Weighting Functions versus Loop Shaping 50
2.4.7 H∞ Controller Search Procedure 57
2.5 AUV Autopilot Controller 58
2.5.1 Depth-control Algorithm 58
2.5.2 Altitude-control Algorithm 61
2.5.3 Heading-control Algorithm 63
Chapter 3 Simulation Analysis 69
3.1 Model Simplifications 69
3.1.1 Diving-control System 69
3.1.2 Steering-control System 70
3.2 Performance Analysis 72
3.2.1 Frequency-domain Response 72
3.2.2 Time-domain Response 75
3.2.3 Interference Noise Simulation 78
3.2.4 Path Tracking 82
3.3 Collision Avoidance Control Simulations 87
3.3.1 Depth-control Simulation 87
3.3.2 Altitude-control Simulation 90
3.3.3 Heading-control Simulation 93
Chapter 4 Experimental Results 96
4.1 Configuration of Altimeters 96
4.2 Bathymetric Measurement 97
4.3 Depth-control Trial 98
4.4 Altitude-control Trial 100
4.5 Heading-control Trial 101
4.5.1 Heading Angle-tracking Test 101
4.5.2 Cross-tracking Control Test 103
4.5.3 Obstacle Boundary–detection Test 104
4.5.4 U-shaped Type I Obstacle-avoidance Test 106
4.5.5 U-shaped Type II Obstacle-avoidance Test 108
Chapter 5 Conclusions and Future Work 110
References 113
Appendix A H∞ variational approach 118
Appendix B Kalman filter design and DVL measurement 126
Appendix C Obstacle Detection Technique 133


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