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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 89-95.doi: 10.6040/j.issn.1672-3961.0.2017.273

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基于L2范数最小估计的无人机飞控系统故障检测

陈杰1,钟麦英2*,张利刚3   

  1. 1. 北京航空航天大学仪器科学与光电工程学院, 北京 100191;2. 山东科技大学电气与自动化工程学院, 山东 青岛 266590;3. 北京航空航天大学无人系统研究院, 北京100191
  • 收稿日期:2017-02-10 出版日期:2017-10-20 发布日期:2017-02-10
  • 通讯作者: 钟麦英(1965— ),女,山东淄博人,教授,博士生导师,主要研究方向为故障诊断.E-mail: myzhong@buaa.edu.cn E-mail:jiechen@buaa.edu.cn
  • 作者简介:陈杰(1994— ),男,陕西汉中人,博士研究生,主要研究方向为故障诊断.E-mail: jiechen@buaa.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61333005,61421063);山东省泰山学者优势特色学科人才团队资助项目

Fault detection of unmanned aerial vehicle flight control system based on optimal estimation of the L2-norm

CHEN Jie1, ZHONG Maiying2*, ZHANG Ligang3   

  1. 1. School of Instrument Science and Opto-electronic Engineering, Beihang University, Beijing 100191, China;
    2. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;
    3. Unmanned Systems Research Institute, Beihang University, Beijing 100191, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

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摘要： 为了实现无人机飞行控制系统的快速在线故障检测,提出一种基于L2范数最小估计的无人机非线性飞行控制系统快速故障检测方法。 建立无人机飞行控制系统的非线性故障模型,并将未知输入的L2范数最小估计值作为残差评价函数,对系统故障进行检测。在针对线性离散时变系统故障检测方法研究的基础上,利用Krein空间投影实现残差评价函数的递推计算以减小故障检测计算量。 以无人机升降舵及速率陀螺故障检测为例,对算法进行仿真试验验证。试验结果表明:该方法可以快速有效的实现无人机飞行控制系统故障检测,为无人机的安全飞行提供可靠的保障。

关键词: 非线性系统, 无人机, 故障检测, 最小估计, 未知输入

Abstract: In order to realize the rapid online failt detection of unmanned aerial vehicle(UAV)flight control system, a fault detection approach based on optimal estimation of the L2-norm was proposed to the fault detection(FD)of UAV nonlinear flight control system. The nonlinear fault model of UAV flight control system was established, and an optimal estimation of the L2-norm of the unknowninputs was found to be the evaluation function for FD. On the foundation of the approach for linear discrete time-varying systems, the projection in Krein space was applied to calculate the evaluation function recursively, and thus the heavy online computational burden could be solved. The FD for UAV elevator and rate gyros was taken as an example to demonstrate the effectiveness of the proposed method. The results showed that the faults of the UAV flight control system could be detected rapidly through the proposed approach, and the safety of UVA could be guaranteed reliably.

Key words: nonlinear systems, optimal estimation, unmanned aerial vehicle, unknown inputs, fault detection

中图分类号: 

  • TP206
[1] QIAN M S, JIANG B, LIU H T. Dynamic surface active fault tolerant control design for the attitude control systems of UAV with actuator fault[J]. International Journal of Control Automation & Systems, 2016, 14(3):723-732.
[2] KIM S H, NEGSH L, CHOI H L. Cubature Kalman filter based fault detection and isolation for formation control of multi-UAVs[J]. IFAC-Papers OnLine, 2016, 49(15):63-68.
[3] CORK L, WALKER R. Sensor fault detection for UAVs using a nonlinear dynamic model and the IMM-UKF algorithm[C] //Information, Decision and Control.[s.n.] :IEEE, 2007:230-235.
[4] HANSEN S, BLANKE M. Diagnosis of Airspeed Measurement Faults for Unmanned Aerial Vehicles[J]. IEEE Transactions on Aerospace & Electronic Systems, 2014, 50(1):224-239.
[5] YANG Y, DING S X, LI L. Parameterization of nonlinear observer-based fault detection systems[J]. IEEE Transactions on Automatic Control, 2016,61(11):3687-3692.
[6] LIU X, GAO X, JIAN H. Robust unknown input observer based fault detection for high-order multi-agent systems with disturbances[J]. Isa Transactions, 2016, 61:15-28.
[7] BELLALI B, HAZZAB A, BOUSSERHANE I K, et al. Parameter estimation for fault diagnosis in nonlinear systems by ANFIS[J]. Procedia Engineering, 2016, 29(4):2016-2021.
[8] WU C, QI J, SONG D, et al. Simultaneous state and parameter estimation based actuator fault detection and diagnosis for an unmanned helicopter[J]. International Journal of Applied Mathematics & Computer Science, 2015, 25(1):175-187.
[9] ZHAI S, WANG W, YE H. Fault diagnosis based on parameter estimation in closed-loop systems[J]. Control Theory & Applications Iet, 2015, 9(7):1146-1153.
[10] ZHONG M, DING S X, HAN Q L, et al. Parity space-based fault estimation for linear discrete Time-Varying systems[J]. IEEE Transactions on Automatic Control, 2010, 55(7):1726-1731.
[11] LEE W H, KIM K H, CHAN G P, et al. Two-faults detection and isolation using extended parity space approach[J]. Journal of Electrical Engineering & Technology, 2012, 7(3):411-419.
[12] DING S X, DING E L, JEINSCH T, et al. An approach to a unified design of FDI systems[C] // Proc of the 3rd Asian Control Conference. Shanghai, China:2000:2812-2817.
[13] ZHONG M Y, DING S X, ZHOU D H. A new scheme of fault detection for linear discrete Time-Varying systems[J]. IEEE Transactions on Automatic Control, 2016, 61(9):2597-2602.
[14] WANG Z, RODRIGUES M, THEILLIOL D, et al. Faultestimation for a class of discrete-time nonlinear system[C] // Preprints of the 19th World Conress. the IFAC. Cape Town, South Africa, 2014:8018-8023.
[15] ZHONG M Y, DING S X, DING E L. Optimal fault detection for linear discrete time-varying systems[J]. Automatica, 2010, 46(8): 1395-1400.
[16] ZHONG M Y, LIU H, SONG N F. On designing an extended H-/H∞ FDF for a class of nonlinear systems[C] // The 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes.[S.L.] , IEEE, 2015:707-712.
[17] KAILATH T, SAYED A H, HASSIBI B. Linear Estimation[M]. New Jersey: Prentice Hall, 1999.
[18] 刘晓东, 钟麦英, 柳海. 基于EKF的无人机飞行控制系统故障检测[J]. 上海交通大学学报, 2015, 49(6):884-888. LIU Xiaodong, ZHONG Maiying, LIU Hai. EKF-Based fault detection of unmanned aerial vehicle flight control system[J]. Journal of Shanghai Jiao Tong University, 2015, 49(6):884-888.
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