簡易檢索 / 詳目顯示

回結果列表
研究生： 羅馬
Chardon, Romain
論文名稱： 應用梯形直接搭配法於垂直起降無人機最佳化傾轉軌跡研究
Transition Trajectory Optimization for a Tailsitter VTOL Using Trapezoid Direct Collocation
指導教授： 賴盈誌
Lai, Ying-Chih
學位類別： 碩士
Master
系所名稱： 工學院 - 航空太空工程學系
Department of Aeronautics & Astronautics
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 87
中文關鍵詞： VTOL（垂直起降） 、軌跡優化 、梯形直接配點 、前進/後退過渡
外文關鍵詞： VTOL, Trajectory optimization, Trapezoid direct collocation, Forward/Backward Transition
相關次數： 點閱：79  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 無人機如今在我們的社會中扮演著重要的角色，它們的應用也越來越多樣化。有些特定任務可能需要能夠垂直起降而無需依賴跑道的固定翼無人機。這些無人機被稱為垂直起降（VTOL）無人機，需要在懸停和巡航模式之間進行複雜的機動。本研究的重點是優化一種名為 Tailsitter 的 VTOL 飛翼的過渡時間、功率和高度範圍。為了實現這一目標，使用了基於 MATLAB 的梯形直接配點軌跡優化方法。根據優化目標，使用不同的成本函數和邊界約束。所研究的 Tailsitter 得到了開源自動駕駛軟件 PX4-Autopilot 和模擬器 Gazebo 的支持。它的過渡動力學在 MATLAB 上重新創建，簡化為2D，並實施在軌跡優化算法中。首先將 MATLAB 上的過渡優化結果與在 Gazebo 上模擬的 PX4-Autopilot 的過渡進行比較。然後，通過使用一個 Python 腳本在 Gazebo 上進行離線模擬，發送俯仰和推力指令，將 MATLAB 的結果近似複製在 Gazebo 上。軌跡優化結果顯示了實現更快和更節能的過渡以及減少高度範圍的可能性。此外，結果還表明在這類過渡中存在不同的可能方法，這可能根據所需任務而帶來優勢。

    Drones are now playing an important role in our society, and their applications are becoming increasingly diverse. Some specific missions may require fixed-wing drones capable of vertical take-off and landing without relying on a runway. These drones are known as vertical take-off landing (VTOL) drones and require sophisticated maneuvering between hover and cruise modes. This research focuses on optimizing the time, power, and altitude range of transitions for a VTOL flying wing called Tailsitter. To achieve this objective, a trajectory optimization method using trapezoid direct collocation on MATLAB is employed. Different cost functions and boundary constraints are used based on the optimization objectives. The studied Tailsitter is supported by the open-source autopilot software PX4-Autopilot and the simulator Gazebo. Its transition dynamic is recreated, simplified in 2D, and implemented in the trajectory optimization algorithm on MATLAB. The results of the transitions optimization obtained on MATLAB are first compared with the PX4-Autopilot’s transitions simulated on Gazebo. Then, the MATLAB results are approximately replicated on Gazebo through an offboard mode simulation using a Python script that sends pitch and thrust commands. The trajectory optimization results demonstrate the possibility of achieving faster and more power-efficient transitions with a reduced altitude range. Additionally, the results show that there are different possible approaches for these types of transitions, which can be an advantage depending on the required mission.

    ABSTRACT I ACKNOWLEDGMENTS III CONTENTS IV TABLES VI FIGURES VII SYMBOLS XI CHAPTER 1 - PREREQUISITES 1 1.1 INTRODUCTION 1 1.2 BACKGROUND 2 1.2.1 Types of VTOL 2 1.2.2 The controller’s importance during the transition of a VTOL drone 3 1.2.3 Different methods to improve the transitions 5 1.2.4 Different trajectory optimization methods 5 1.3 MOTIVATIONS AND GOAL 6 1.3.1 Motivations 6 1.3.2 Goals 7 1.4 LITTERATURE REVIEW 8 1.5 SUMMARY 11 1.6 SETTINGS OF THE ENVIRONMENTS 11 CHAPTER 2: TAILSITTER’S DYNAMIC MODEL 13 2.1 SYSTEM OVERVIEW 13 2.1.1 Configuration 13 2.1.2 Flight envelope 14 2.1.3 Working frame 15 2.2 FLIGHT DYNAMIC EQUATIONS OF THE TRANSITIONS 16 2.2.1 Equations 16 2.2.2 Thrust forces 19 2.2.3 Aerodynamic forces 20 2.3 AERODYNAMIC COEFFICIENTS 21 2.3.1 Wing aerodynamic parameters (geometry, Reynold, and Ncrit numbers) 22 2.3.2 Computation of the aerodynamic coefficients on Xfoil 24 2.3.3 Elevators deflection aerodynamic coefficients 25 2.3.4 Final dynamic 30 CHAPTER 3: METHODOLOGY 32 3.1 TRAPEZOID COLLOCATION TRAJECTORY OPTIMIZATION 33 3.1.1 Trajectory optimization problem 33 3.1.2 Direct trapezoidal collocation process 34 3.1.3 Values discretization 35 3.1.4 Integral of the cost function transformation 36 3.1.5 System dynamics transformation to quadrature form 37 3.1.6 Discretization of the constraints 38 3.1.7 Final NLP 38 3.1.8 Trapezoidal interpolation 39 3.1.9 Error estimation 43 3.2 DESCRIPTION OF THE OPTIMIZED TRANSITIONS 44 3.2.1 Definition of the reference transitions and the 5 optimized transitions 44 3.2.2 Design of the optimized transitions 46 3.2.3 Boundary constraints of the 5 optimized transitions 48 3.3 MATLAB PROGRAM OF THE OPTIMIZATIONS 52 3.3.1 Dynamic 52 3.3.2 Cost functions 52 3.3.3 Boundary Constraints 53 3.3.4 Optimization parameters 54 3.3.5 Command window after the optimization 54 3.4 APPROXIMATED REPRESENTATION OF THE RESULT IN A REAL ENVIRONMENT SIMULATION (GAZEBO) 56 3.4.1 Simulation of the Tailsitter supported by PX4 in offboard mode and run on Gazebo 56 3.4.2 Build the whole simulation with ROS and communicate with MAVROS 57 3.4.3 Script of the simulation written in Python for the offboard mode 58 3.4.4 Collect the flight data and launch the reference transitions in automatic mode with QGroundControl 59 3.4.5 Flowchart of the used softwares 60 CHAPTER 4: SIMULATION RESULTS 62 4.1 RESULTS OF THE FORWARD TRANSITIONS OPTIMIZATION 62 4.1.1 States 62 4.1.2 Control inputs 65 4.1.3 Cost 67 4.1.4 Forces acting on the system 67 4.1.5 Behavior of the Tailsitter during the transitions 69 4.1.6 Error 70 4.2 APPROXIMATED REPRODUCTION OF THE FORWARD TRANSITIONS 3,4 IN REAL CONDITIONS ON GAZEBO 72 4.2.1 Comparison between the reference and the reproduced transitions 72 4.2.2 Comparison between the optimization and the reproduced transitions 75 4.3 RESULTS OF THE BACKWARD TRANSITION OPTIMIZATION 76 4.3.1 States 76 4.3.2 Control inputs 78 4.4 APPROXIMATED REPRODUCTION OF THE BACKWARD TRANSITION IN REAL CONDITIONS ON GAZEBO 80 CONCLUSION 83 REFERENCES 85

    1. Tailsitter studied supported by the open-sources gazebo and PX4-Autopilot. Available from: https://docs.px4.io/main/en/sim_gazebo_classic/gazebo_vehicles.html#tailsitter_vtol.
    2. Masuda, K. and K. Uchiyama, Robust Control Design for Quad Tilt-Wing UAV. Aerospace, 2018. Volume 5(1).
    3. Clements, T. and M. Rais-Rohani, Design optimization of a composite tilt-rotor wing with stability approximations, in 41st Structures, Structural Dynamics, and Materials Conference and Exhibit. 2000.
    4. Narayana Namasivayam, S., et al., Design and Fabrication of Small Vertical-Take-Off-Landing Unmanned Aerial Vehicle. MATEC Web of Conferences, 2018. Volume 152.
    5. PX4's controller diagrams. Available from: https://docs.px4.io/v1.12/en/flight_stack/controller_diagrams.html.
    6. Flores, A. and G. Flores, Implementation of a Neural Network for Nonlinearities Estimation in a Tail-Sitter Aircraft. Journal of Intelligent & Robotic Systems, 2021. Volume 103(2).
    7. Flores, A. and G. Flores. Transition control of a tail-sitter UAV using recurrent neural networks. in International Conference on Unmanned Aircraft Systems (ICUAS). 2020. IEEE.
    8. Rao, A.V., A survey of numerical methods for optimal control. Advances in the Astronautical Sciences, 2009. Volume 135(1): p. 497-528.
    9. "Optim traj" library. Available from: https://www.mathworks.com/matlabcentral/fileexchange/54386-optimtraj-trajectory-optimization-library.
    10. Chiappinelli, R. and M. Nahon. Modeling and Control of a Tailsitter UAV. in International Conference on Unmanned Aircraft Systems (ICUAS). 2018. IEEE.
    11. Flores, A., A.M. de Oca, and G. Flores. A simple controller for the transition maneuver of a tail-sitter drone. in IEEE Conference on Decision and Control (CDC). 2018. IEEE.
    12. Knoebel, N.B. and T.W. McLain. Adaptive quaternion control of a miniature tailsitter UAV. in American Control Conference. 2008. IEEE.
    13. Lyu, X., et al., Design and implementation of a quadrotor tail-sitter VTOL UAV, in IEEE International Conference on Robotics and Automation (ICRA). 2017. p. 3924-3930.
    14. Reddinger, J.-P., et al. Modeling and trajectory control of a transitioning quadrotor biplane tailsitter. in Proceedings of the Vertical Flight Society 75th Annual Forum, Philadelphia, PA, USA. 2019.
    15. Ritz, R. and R. D'Andrea. A global controller for flying wing tailsitter vehicles. in IEEE international conference on robotics and automation (ICRA). 2017. IEEE.
    16. Tal, E. and S. Karaman, Global Incremental Flight Control for Agile Maneuvering of a Tailsitter Flying Wing. Journal of Guidance, Control, and Dynamics, 2022. Volume 45(12): p. 2332-2349.
    17. Verling, S., et al. Model-based transition optimization for a VTOL tailsitter. in IEEE International Conference on Robotics and Automation (ICRA). 2017. IEEE.
    18. Verling, S., et al. Full attitude control of a VTOL tailsitter UAV. in IEEE international conference on robotics and automation (ICRA). 2016. IEEE.
    19. Zhang, F., et al., Modeling and Flight Control Simulation of a Quadrotor Tailsitter VTOL UAV, in AIAA Modeling and Simulation Technologies Conference. 2017.
    20. Domitran, S. and M.B. Babac. A Machine Learning Approach to Flight Control of a VTOL Tailsitter UAV. in 44th International Convention on Information, Communication and Electronic Technology (MIPRO). 2021. IEEE.
    21. Li, B., et al., Transition Optimization for a VTOL Tail-Sitter UAV. IEEE/ASME Transactions on Mechatronics, 2020. Volume 25(5): p. 2534-2545.
    22. Cook, M.V., Flight dynamics principles: a linear systems approach to aircraft stability and control. 2012: Butterworth-Heinemann.
    23. Benson, D.A., et al., Direct Trajectory Optimization and Costate Estimation via an Orthogonal Collocation Method. Journal of Guidance, Control, and Dynamics, 2006. 29(6): p. 1435-1440.
    24. Herman, A.L. and B.A. Conway, Direct optimization using collocation based on high-order Gauss-Lobatto quadrature rules. Journal of Guidance, Control, and Dynamics, 1996. Volume 19(3): p. 592-599.
    25. Jackson, B., Exploring Quadrature Rules in Direct Collocation Methods for Trajectory Optimization. 2018, Tech. Rep.
    26. Mao, R., H. Gao, and L. Guo. Optimal motion planning of differential-drive mobile robots based on trapezoidal collocation method. in Journal of Physics: Conference Series. 2019. IOP Publishing.
    27. Von Stryk, O. and R. Bulirsch, Direct and indirect methods for trajectory optimization. Annals of operations research, 1992. Volume 37: p. 357-373.
    28. Kelly, M., An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation. SIAM Review, 2017. Volume 59(4): p. 849-904.
    29. Kelly, M.P., OptimTraj user’s guide, Version 1.5. 2019.
    30. Drela, M., XFOIL: An Analysis and Design System for Low Reynolds Number Airfoils, in Low Reynolds Number Aerodynamics. 1989. p. 1-12.
    31. Wick, P., Peter Wicks Airfoils for Plank Planforms. Radio Controlled Soaring Digest, 2005. Volume 22: p. 39-44.
    32. Zhang, X., et al., A Survey of Modelling and Identification of Quadrotor Robot. Abstract and Applied Analysis, 2014: p. 1-16.
    33. Yoo, D.-W., et al., Dynamic Modeling and Stabilization Techniques for Tri-Rotor Unmanned Aerial Vehicles. International Journal of Aeronautical and Space Sciences, 2010. Volume 11(3): p. 167-174.
    34. Wind tunnel image. Available from: https://en.wikipedia.org/wiki/File:MD-11_12ft_Wind_Tunnel_Test.jpg.
    35. PW75 Airfoil. Available from: http://airfoiltools.com/airfoil/details?airfoil=pw75-pw.
    36. Lissaman, P., Low-Reynolds-number airfoils. Annual review of fluid mechanics, 1983. Volume 15(1): p. 223-239.

    無法下載圖示 校內：2028-06-14公開
    校外：2028-06-14公開
    電子論文尚未授權公開，紙本請查館藏目錄
    QR CODE