您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 150-156.doi: 10.6040/j.issn.1672-3961.0.2017.270

• • 上一篇    下一篇

基于等价空间的无人机飞行控制系统故障检测

赵煊1,钟麦英2*,郭丁飞1   

  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
  • 收稿日期:2017-05-27 出版日期:2017-10-20 发布日期:2017-05-27
  • 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:zhaoxuan17@buaa.edu.cn
  • 作者简介:赵煊(1993— ),男,内蒙古赤峰人,硕士研究生,主要研究方向为故障诊断. E-mail:zhaoxuan17@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

Parity space-based fault detection for unmanned aerial vehicle flight control systems

ZHAO Xuan1, ZHONG Maiying2*, GUO Dingfei1   

  1. 1. School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China;
    2. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
  • Received:2017-05-27 Online:2017-10-20 Published:2017-05-27

摘要: 无人机(unmanned aerial vehicle, UAV)飞行控制系统的故障检测,对于保障无人机的飞行安全具有重要意义。等价空间方法具有残差与未知初始状态解耦的优势,但随着等价空间阶次的提高,其在线计算量显著增大。针对上述问题,提出一种基于等价空间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,在针对线性离散时变系统的等价空间故障检测方法研究的基础上,利用Krein空间投影来实现残差评价函数的递推计算以减小故障检测计算量。以无人机空速管及升降舵故障检测为例,对算法进行了仿真试验验证。试验结果表明,提出的方法可以实现无人机飞行控制系统的快速故障检测。

关键词: 故障检测, 等价空间, 无人机, 非线性系统

Abstract: The fault detection(FD)for unmanned aerial vehicle(UAV)flight control system is of great significance to ensure the flight safety of UAV. The parity space approach has the advantage of the decoupling of residual and unknown initial state. However, the increasing of parity order will lead to heavy computational task. Aiming at these problems, a modified parity space approach was put forward for the FD of UAV nonlinear flight control systems. The nonlinear fault model of UAV flight control system was established. On the foundation of parity space 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 pitot tube and elevator 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.

Key words: nonlinear systems, fault detection, parity space, unmanned aerial vehicle

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, 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] // Proceedings of the 3rd Asian Control Conference. Shanghai, China:ASCC, 2000:2812-2817.
[13] 桂卫华, 彭涛, DING Steven X,等. 基于传感器最优配置的等价空间故障检测方法[J]. 控制与决策, 2007, 22(7):800-804. GUI Weihua, PENG Tao, DING Steven X, et al. Parity space approach to fault detection based on optimal sensor location[J]. Control and Decision, 2007, 22(7):800-804.
[14] MAGRABI S M, GIBBENS P W. Decentralized fault detection and diagnosis in navigation systems for unmanned aerial vehicles[C] // Position Location and Navigation Symposium. San Diego, USA: IEEE, 2000: 363-370.
[15] 钟麦英, 薛婷. 基于观测器与小波变换的UAV作动器故障检测[J]. 系统仿真技术, 2016, 12(1):6-12. ZHONG Maiying, XUE Ting. Obeserver and wavelet transform based actuator fault detection for UAV[J]. System Simulation Technology, 2016, 12(1):6-12.
[16] 马岩, 曹金成, 黄勇,等. 基于BP神经网络的无人机故障诊断专家系统研究[J]. 长春理工大学学报(自然科学版), 2011, 34(4):137-139. MA Yan, CAO Jincheng, HUANG Yong, et al. A combined method based on expert system and BP neural network for UAV systems fault diagnosis[J]. Journal of Changchun University of Science and Technology(Natural Science Edition), 2011, 34(4):137-139.
[17] SAMY I, POSTLETHWAITE I, GU D. Neural network based sensor validation scheme demonstrated on an unmanned air vehicle(UAV)model[C] //Proceedings of the 48th IEEE Conference on Decision and Control, CDC 2009. Shanghai, China:IEEE, 2009: 1237-1242.
[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.
[19] ZHONG M Y, ZHOU D H, DING S X. On H∞designing fault detection filter for linear discrete time-varying systems[J]. IEEE Transactions on Automatic Control, 2010, 55(7):1689-1695.
[20] ZHONG M, SONG Y, DING S X. Parity space-based fault detection for linear discrete time-varying systems with unknown input[J]. Automatica, 2015, 59(1):120-126.
[21] BATEMAN F, NOURA H, OULADSINE M. Fault diagnosis and fault-tolerant control strategy for the aerosonde UAV[J]. IEEE Transactions on Aerospace & Electronic Systems, 2011, 47(3):2119-2137.
[22] DING S X, JEINSCH T, DING E L. An approach to analysis and design of observer and parity space relation based FDI systems[C] // Proceedings of the 14th IFAC World Congress, Beijing, China:IFAC, 1999.
[1] 刘洋. 乘性故障对开闭环系统故障诊断性能的影响[J]. 山东大学学报(工学版), 2017, 47(5): 38-43.
[2] 杨瑞. 基于稀疏表示的间歇故障检测方法及仿真[J]. 山东大学学报(工学版), 2017, 47(5): 51-56.
[3] 李炜,王可宏,曹慧超. 基于新型ESF的一类非线性系统故障滤波方法[J]. 山东大学学报(工学版), 2017, 47(5): 7-14.
[4] 李洪阳,何潇. 基于SCKF方法的非线性随机动态系统故障诊断方法[J]. 山东大学学报(工学版), 2017, 47(5): 130-135.
[5] 陈志文, 彭涛, 阳春华, 何章鸣,杨超, 杨笑悦. 基于改进的典型相关分析的故障检测方法[J]. 山东大学学报(工学版), 2017, 47(5): 44-50.
[6] 张米露,王天真,汤天浩,辛斌. 一种模式关联主元分析的海流机故障检测方法[J]. 山东大学学报(工学版), 2017, 47(5): 123-129.
[7] 陈杰,钟麦英,张利刚. 基于L2范数最小估计的无人机飞控系统故障检测[J]. 山东大学学报(工学版), 2017, 47(5): 89-95.
[8] 李明虎,李钢,钟麦英. 动态核主元分析在无人机故障诊断中的应用[J]. 山东大学学报(工学版), 2017, 47(5): 215-222.
[9] 王常顺,肖海荣. 基于自抗扰控制的水面无人艇路径跟踪控制器[J]. 山东大学学报(工学版), 2016, 46(4): 54-59.
[10] 王飞飞1,闫雪华2*,刘允刚3. 一类控制系数未知非线性参数化系统的
输出反馈实际跟踪控制
[J]. 山东大学学报(工学版), 2013, 43(5): 55-67.
[11] 尚芳 刘允刚 张承慧. 一类不确定非线性系统输出反馈扰动抑制[J]. 山东大学学报(工学版), 2010, 40(1): 19-27.
[12] 张健 刘允刚. 一类不确定高阶随机非线性系统的自适应镇定[J]. 山东大学学报(工学版), 2009, 39(6): 35-47.
[13] 周风余,单金明,王伟,陈景帅,阮久宏. 基于ADRC的船舶航向控制器设计与仿真研究[J]. 山东大学学报(工学版), 2009, 39(1): 57-62.
[14] 方 挺,杨 忠,沈春林 . 无人机编队视频序列中的多目标精确跟踪[J]. 山东大学学报(工学版), 2008, 38(4): 22-26 .
[15] 贾秀芹,刘允刚 . 非线性系统的 H部分状态观测器设计[J]. 山东大学学报(工学版), 2007, 37(5): 40-46 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!