无人机桨叶损伤的在线模型估计新方法

doi:10.7527/S1000-6893.2019.23316

Abstract

Abstract: Highly complicated environments usually lead to blade impairment of unmanned rotor aerial vehicle,causing deleterious impact on control performance and stability, even inducing catastrophic consequences. Online estimate and reestablishment of the dynamic model in the presence of blade impairment are solid foundations of ensuring the stability of the post-fault unmanned rotor aerial vehicle. Nevertheless, since the blade impairments are inside the unmanned rotor aerial vehicle, its low visibility renders the typical observer method unable to achieve estimation. This study presents a novel penetrating disturbance observer that can estimate the blade damage via the establishment of a penetrating function and the projection of the blade damage. Moreover, the convergence of estimation error is verified and the model uncertainty is quantitatively analyzed. The hardware-in-the-loop platform based on a quadrotor unmanned aerial vehicle is established to validate the effectiveness of the proposed method. The experiment results exemplify that the developed method can estimate the disturbance induced by blade impairments, overcoming the difficulty of estimating torque under the condition of blade impairment, thereby achieving the model recognition.

Key words: online modeling, quantitative analysis of model uncertainty, disturbance observer, blade impairment, unmanned aerial vehicle

CLC Number:

ZHANG Xiao, NI Ming, YU Xiang, GUO Lei. A novel estimation method for blade impairments of unmanned aerial vehicle[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(1): 323316-323316.

References 22

[1]	CAI G W, JORGE D, LAKMAL S. A survey of small-scale unmanned aerial vehicles:Recent advances and future development trends[J]. Unmanned Systems, 2014, 2(2):175-199.
[2]	MUELLER M W, D'ANDREA R. Stability and control of a quadrocopter despite the complete loss of one, two, or three propellers[C]//IEEE International Conference on Robotics & Automation (ICRA). Piscataway, NJ:IEEE Press, 2014:45-52.
[3]	ZHANG W, MUELLER M W, D'ANDREA R. A controllable flying vehicle with a single moving part[C]//IEEE International Conference on Robotics and Automation (ICRA). Piscataway, NJ:IEEE Press, 2016:3275-3281.
[4]	MUELLER M W, D'ANDREA R. Relaxed hover solutions for multicopters:Application to algorithmic redundancy and novel vehicles[J]. The International Journal of Robotics Research, 2016, 35(8):873-889.
[5]	DYDEK Z T, ANNASWAMY A M, LAVRETSKY E. Adaptive control of quadrotor UAVs:A design trade study with flight evaluations[J]. IEEE Transactions on Control Systems Technology, 2012, 21(4):1400-1406.
[6]	ZHANG Y M, JIANG J. Bibliographical review on reconfigurable fault-tolerant control systems[J]. Annual Reviews in Control, 2008, 32(2):229-252.
[7]	ZHANG Y M, CHAMSEDDINE A, RABBATH C A, et al. Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed[J]. Journal of the Franklin Institute, 2013, 350(9):2396-2422.
[8]	CHEN W H, BALLANCE D J, GAWTHROP P J, et al. A nonlinear disturbance observer for robotic manipulators[J]. IEEE Transactions on Industrial Electronics, 2000, 47(4):932-938.
[9]	GUO L, CHEN W H. Disturbance attenuation and rejection for systems with nonlinearity via DOBC approach[J]. International Journal of Robust and Nonlinear Control, 2005, 15(3):109-125.
[10]	王发威, 董新民, 陈勇, 等.多操纵面飞机舵面损伤的快速故障诊断[J]. 航空学报, 2015, 36(7):2350-2360. WANG F W, DONG X M, CHEN Y, et al. Fast fault diagnosis of multi-effectors aircraft with control surface damage[J]. Acta Aeronauticaeta Astronautica Sinica, 2015, 36(7):2350-2360(in Chinese).
[11]	ROTONDO D, CRISTOFARO A, JOHANSEN T A, et al. Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers[J]. International Journal of Robust and Nonlinear Control, 2019, 29(16):5456-5480.
[12]	LU H, LIU C, GUO L, et al. Flight control design for small-scale helicopter using disturbance-observer-based backstepping[J]. Journal of Guidance, Control, and Dynamics, 2015, 38(11):2235-2240.
[13]	ZHANG J, ZHOU L, LI C, et al. Binary observers based control for quadrotor unmanned aerial vehicle with disturbances and measurement delay[J]. Proceedings of the Institution of Mechanical Engineers, Part G:Journal of Aerospace Engineering, 2018, 232(15):2807-2820.
[14]	CAYERO B J F, ROTONDO D, MORCEGO S B, et al. Optimal state observation using quadratic boundedness:Application to UAV disturbance estimation[J]. International Journal of Applied Mathematics and Computer Science, 2019, 29(1):99-109.
[15]	LU Q, REN B, PARAMESWARAN S. Shipboard landing control enabled by an uncertainty and disturbance estimator[J]. Journal of Guidance, Control, and Dynamics, 2018, 41(7):1502-1520.
[16]	BESNARD L, SHTESSEL Y B, LANDRUM B. Quadrotor vehicle control via sliding mode controller driven by sliding mode disturbance observer[J]. Journal of the Franklin Institute, 2012, 349(2):658-684.
[17]	CHENG Y, JIANG L, LI T, et al. Robust tracking control for a quadrotor UAV via DOBC approach[C]//2018 Chinese Control and Decision Conference (CCDC), 2018:559-563.
[18]	KIM J, KANG M, PARK S, et al. Accurate modeling and robust hovering control for a quadrotor VTOL aircraft[J]. Journal of Intelligent and Robotic Systems, 2010, 57(1):9-26.
[19]	MA D, XIA Y, LI T, et al. Active disturbance rejection and predictive control strategy for a quadrotor helicopter[J]. IET Control Theory & Applications, 2016, 10(17):2213-2222.
[20]	LYU X, ZHOU J, GU H, et al. Disturbance observer based hovering control of quadrotor tail-sitter VTOL UAVs using H∞ synthesis[J]. IEEE Robotics and Automation Letters, 2018, 3(4):2910-2917.
[21]	MAHONY R, KUMAR V, CORKE P. Multirotor aerial vehicles:Modeling, estimation, and control of quadrotor[J]. IEEE Robotics & Automation Magazine, 2012, 19(3):20-32.
[22]	郑大钟. 线性系统理论[M]. 北京:清华大学出版社, 2002:238-242. ZHENG D Z. Linear systom theory[M]. Beijing:Tsinghua University Press, 2002:238-242(in Chinese).

A novel estimation method for blade impairments of unmanned aerial vehicle

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References 22

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Yuanliang XUE, Guodong JIN, Lining TAN, Jiankun XU. Adaptive UAV target tracking algorithm based on multi-scale fusion [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(1): 326107-326107.
[2]	HU Yangxiu, ZHAO Changchun, JIA Chenglong, QIAN Zhouyuan, HU Tao. Synchronous path formation control of UAV swarm based on robot operating system (ROS) [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S1): 726914-726914.
[3]	HU Wei, WAN Wenzhang, CHEN Mou. Neural network and disturbance observer based control for automatic carrier landing of UAV [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S1): 726963-726963.
[4]	LI Hui, LONG Teng, SUN Jingliang, XU Guangtong. Adaptive line-of-sight method for 3D path following of UAVs [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 326105-326105.
[5]	XING Ling, JIA Xiaofan, ZHAO Pengcheng, WU Honghai. Location privacy protection scheme for unmanned aerial vehicle group based on matrix encryption [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 325386-325386.
[6]	ZHANG Zhouyu, CAO Yunfeng, FAN Yanming. Research progress of vision based aerospace conflict sensing technologies for small unmanned aerial vehicle in low altitude [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 25645-025645.
[7]	LI Chunpeng, ZHANG Tiejun, QIAN Zhansen, LIU Tiezhong. Aerodynamic design of modular configuration for multi-mission unmanned aerial vehicle [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(7): 125411-125411.
[8]	LIU Fang, SUN Yanan. UAV target tracking algorithm based on adaptive fusion network [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(7): 325522-325522.
[9]	SUN Qin, LI Hongxu, WANG Yuzhi, ZHOU Liping, ZHANG Yingchao. Resilient UAV swarm modeling and solving based on multi-domain collaborative method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 325340-325340.
[10]	LIU Fang, HAN Xiao. Adaptive aerial object detection based on multi-scale deep learning [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 325270-325270.
[11]	LI Xia, QI Guoyuan, GUO Xitong, ZHAO Xu. High-order differential feedback control and its application in quadrotor UAV [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(12): 326047-326047.
[12]	LI Jie, ZHANG Heng, YANG Zhao. Trade-off aerodynamic design of basic wing for demonstrator UAVs with special layout for high-subsonic laminar flow verification [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(11): 526786-526786.
[13]	GUO Hongzhen, CHEN Mou. Safety formation control of quadrotor UAVs based on prescribed performance [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(8): 525789-525789.
[14]	GAO Ming, YU Weichen, WANG Shanshan, WANG Rongchuang, SHI Jianjiang. Multidimensional coupled modeling for solar powered UAV energy system [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(7): 224461-224461.
[15]	HU Xinting, WU Yu. Risk-based discrete multi-path planning method for UAVs in urban environments [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(6): 324383-324383.