无人机在复杂高对抗环境中极易出现桨叶结构受损故障，造成无人机控制性能退化甚至失稳，给无人机带来灾难性后果。桨叶结构损伤条件下无人机动力学模型的在线估计与重建是保障无人机控制系统稳定的重要前提。由于桨叶损伤干扰隐含于无人机动力学模型的内部，可观测性较低，典型的干扰观测器难以实现对此类干扰的估计。提出了一种新型穿透型干扰观测器，通过构造穿透函数，将模型内部的干扰映射到一个新建的平行空间，实现了对桨叶损伤的估计和故障后无人机的建模，并给出了所设计穿透型干扰观测器的稳定性条件。以四旋翼无人机为应用对象，对桨叶损伤形成的干扰进行在线估计与量化，反演出桨叶随机出现的损伤，实现了桨叶损伤后无人机动力学模型的在线精细重建，解决了无人机桨叶损伤故障下力矩输入难以直接测量的问题。半物理仿真实验验证了所提方法的有效性。
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.
[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).
CJA