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2015 | 25 | 1 | 175-187
Tytuł artykułu

Simultaneous state and parameter estimation based actuator fault detection and diagnosis for an unmanned helicopter

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Simultaneous state and parameter estimation based actuator fault detection and diagnosis (FDD) for single-rotor unmanned helicopters (UHs) is investigated in this paper. A literature review of actuator FDD for UHs is given firstly. Based on actuator healthy coefficients (AHCs), which are introduced to represent actuator faults, a combined dynamic model is established with the augmented state containing both the flight state and AHCs. Then the actuator fault detection and diagnosis problem is transformed into a general nonlinear estimation one: given control inputs and the measured flight state contaminated by measurement noises, estimate both the flight state and AHCs recursively in each time-step, which is also known as the simultaneous state and parameter estimation problem. The estimated AHCs can further be used for fault tolerant control (FTC). Based on the existing widely used nonlinear estimation methods such as the unscented Kalman filter (UKF) and the extended set-membership filter (ESMF), three kinds of adaptive schemes (KF-UKF, MIT-UKF and MIT-ESMF) are proposed by our team to improve the actuator FDD performance. A comprehensive comparative study on these different estimation methods is given in detail to illustrate their advantages and disadvantages when applied to unmanned helicopter actuator FDD.
Rocznik
Tom
25
Numer
1
Strony
175-187
Opis fizyczny
Daty
wydano
2015
otrzymano
2014-02-18
poprawiono
2014-05-06
poprawiono
2014-08-12
Twórcy
autor
  • State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114 Nanta Street, Shenyang, Liaoning Province, China
  • University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China,
autor
  • State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114 Nanta Street, Shenyang, Liaoning Province, China
autor
  • State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114 Nanta Street, Shenyang, Liaoning Province, China
autor
  • State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114 Nanta Street, Shenyang, Liaoning Province, China
  • University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China,
autor
  • State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114 Nanta Street, Shenyang, Liaoning Province, China
Bibliografia
  • Amidi, O., Kanade, T. and Miller , J.R. (1998). Vision-based autonomous helicopter research at Carnegie Mellon Robotics Institute 1991-1997, Proceedings of the American Helicopter Society International Conference, Heli, Japan, pp. T7-3-1-T7-3-12.
  • Amoozgar, M.H., Chamseddine, A. and Zhang, Y. (2013). Experimental test of a two-stage Kalman filter for actuator fault detection and diagnosis of an unmanned quadrotor helicopter, Journal of Intelligent and Robotic Systems 70(4): 107-117.
  • Arne, W. and Jürgen, A. (2011). Robust fault isolation observers for non-square systems-a parametric approach, Proceedings of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Mexico City, Mexico, pp. 1275-1280.
  • Åström, K.J. and Wittenmark, B. (2013). Adaptive Control, Courier Dover Publications, New York, NY.
  • Bätz, G., Weber, B., Scheint, M., Wollherr, D. and Buss, M. (2013). Dynamic contact force/torque observer: Sensor fusion for improved interaction control, The International Journal of Robotics Research 32(4): 446-457.
  • Bian, X., Li, X., Chen, H., Gan, D. and Qiu, J. (2011). Joint estimation of state and parameter with synchrophasors, Part II: Parameter tracking, IEEE Transactions on Power Systems 26(3): 1209-1220.
  • Cai, G., Chen, B., Dong, X. and Lee, T. (2011a). Design and implementation of a robust and nonlinear flight control system for an unmanned helicopter, Mechatronics 21(5): 803-820.
  • Cai, G., Chen, B. and Lee, T. (2011b). Unmanned Rotorcraft System, Springer-Verlag, London.
  • Campbell, M., Lee, J., Scholte, E. and Rathbun, D. (2007). Simulation and flight test of autonomous aircraft estimation, planning, and control algorithms, Journal of Guidance, Control, and Dynamics 30(6): 1597-1609.
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  • Drozeski, G., Saha, B. and Vachtsevanos, G. (2005). A fault detection and reconfigurable control architecture for unmanned aerial vehicles, Proceedings of the IEEE Aerospace Conference, Big City, MT, USA, pp. 1-9.
  • Ducard, G. and Geering, H. (2008). Efficient nonlinear actuator fault detection and isolation system for unmanned aerial vehicles, Journal of Guidance, Control, and Dynamics 31(1): 225-237.
  • He, Y. and Han, J. (2010). Acceleration-feedback-enhanced robust control of an unmanned helicopter, Journal of Guidance, Control, and Dynamics 33(4): 1236-1250.
  • Heredia, G., Ollero, A. and Bejar, M. (2008). Sensor and actuator fault detection in small autonomous helicopters, Mechatronics 18(2): 90-99.
  • Heredia, G., Remu, B. and Ollero, A. (2004). Actuator fault detection in autonomous helicopters, Proceedings of the 5th IFAC Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal, pp. 569-574.
  • Ingimundarson, A., Bravo, J., Puig, V., Alamo, T. and Guerra, P. (2009). Robust fault detection using zonotope-based set-membership consistency test, International Journal of Adaptive Control and Signal Processing 23(4): 311-330.
  • Johnson, E.N. and Schrage, D.P. (2003). The Georgia Tech unmanned aerial research vehicle: GTMax, Proceedings of the AIAA Guidance, Navigation, and Control Conference, Austin, TX, USA, pp. 11-14.
  • Julier, S. and Uhlmann, J. (2004). Unscented filtering and nonlinear estimation, Proceedings of the IEEE 92(3): 401-422.
  • Kandepu, R., Foss, B. and Imsland, L. (2008). Applying the unscented Kalman filter for nonlinear state estimation, Journal of Process Control 18(8): 753-768.
  • Kotecha, J. and Djuric, P. (2003). Gaussian particle filtering, IEEE Transactions on Signal Processing 51(10): 2592-2601.
  • Qi, J., Han, J. and Wu, Z. (2008). Rotorcraft UAV actuator failure estimation with KF-based adaptive UKF algorithm, Proceedings of the American Control Conference, Seattle, WA, USA, pp. 1618-1623.
  • Qi, J., Jiang, Z., Zhao, X. and Han, J. (2007). Adaptive UKF and its application in fault tolerant control of rotorcraft UAV, Proceedings of the AIAA Guidance, Navigation, and Control Conference, Hilton Head, SC, USA, pp. 43-57.
  • Qi, J., Song, D., Dai, L. and Han, J. (2009). Design, implement and testing of a rotorcraft UAV system, in T. Mung Lam (Ed.), Aerial Vehicles, InTech, Rijeka, pp. 537-554.
  • Qi, J., Song, D., Wu, C., Han, J. and Wang, T. (2012). KF-based adaptive UKF algorithm and its application for rotorcraft UAV actuator failure estimation, International Journal of Advanced Robotic Systems 9(132): 1-9.
  • Qi, J., Zhao, X., Jiang, Z. and Han, J. (2006). Design and implement of a rotorcraft UAV testbed, Proceedings of the IEEE International Conference on Robotics and Biomimetics, Kunming, China, pp. 109-114.
  • Qi, X., Qi, J., Theilliol, D., Zhang, Y. and Han, J. (2014). A review on fault diagnosis and fault tolerant control methods for single-rotor aerial vehicles, Journal of Intelligent and Robotic Systems 73(4): 535-555.
  • Qi, X., Theilliol, D., Qi, J., Zhang, Y., Han, J., Song, D., Wang, L. and Xia, Y. (2013). Fault diagnosis and fault tolerant control methods for manned and unmanned helicopters: A literature review, Proceedings of the International Conference on Control and Fault-Tolerant Systems (SysTol), Nice, France, pp. 132-139.
  • Song, D., Wu, C., Qi, J., Han, J. and Wang, T. (2012). A MIT-based nonlinear adaptive set-membership filter for the ellipsoidal estimation of mobile robots' states, International Journal of Advanced Robotic Systems 9(125): 1-12.
  • Van Der Merwe, R. (2004). Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models, Ph.D. thesis, Oregon Health & Science University, Portland, OR.
  • Wu, C., Song, D., Dai, L., Qi, J., Han, J. and Wang, Y. (2010). Design and implementation of a compact RUAV navigation system, Proceedings of the IEEE International Conference on Robotics and Biomimetics, Tianjin, China, pp. 1662-1667.
  • Wu, C., Song, D., Qi, J. and Han, J. (2012). Rotorcraft UAV actuator failure detection based on a new adaptive set-membership filter, Proceedings of the 5th International Conference on Intelligent Robotics and Applications, Montreal, Canada, pp. 433-442.
  • Xiong, J. (2013). Set-Membership State Estimation and Application on Fault Detection, Ph.D. thesis, French National Center for Scientific Research, Toulouse.
  • Xiong, K., Zhang, H. and Chan, C. (2006). Performance evaluation of UKF-based nonlinear filtering, Automatica 42(2): 261-270.
  • Zhang, Y. and Jiang, J. (2008). Bibliographical review on reconfigurable fault-tolerant control systems, Annual Reviews in Control 32(2): 229-252.
  • Zhou, B. and Han, J. (2007). A comparison of nonlinear estimation methods for tracked vehicle with slipping, Proceedings of the IEEE International Conference on Control and Automation, Guangzhou, China, pp. 389-394.
  • Zhou, B., Han, J. and Liu, G. (2008). A UD factorization-based nonlinear adaptive set-membership filter for ellipsoidal estimation, International Journal of Robust and Nonlinear Control 18(16): 1513-1531.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv25i1p175bwm
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