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Warianty tytułu
Języki publikacji
Abstrakty
This paper deals with the problem of robust fault detection (FD) for an unmanned aerial vehicle (UAV) flight control system (FCS). A nonlinear model to describe the UAV longitudinal motions is introduced, in which multiple sources of disturbances include wind effects, modeling errors and sensor noises are classified into groups. Then the FD problem is formulated as fault detection filter (FDF) design for a kind of nonlinear discrete time varying systems subject to multiple disturbances. In order to achieve robust FD performance against multiple disturbances, simultaneous disturbance compensation and Hi/H∞ optimization are carried out in designing the FDF. The optimality of the proposed FDF is shown in detail. Finally, both simulations and real flight data are applied to validate the proposed method. An improvement of FD performance is achieved compared with the conventional Hi/H∞-FDF.
Rocznik
Tom
Strony
349--362
Opis fizyczny
Bibliogr. 38 poz., tab., wykr.
Twórcy
autor
- Department of Inertia Technology and Navigation Instrumentation, Beihang University, Beijing 100191, China
autor
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
autor
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
Bibliografia
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- [3] Caliskan, F., Zhang, Y.,Wu, N.E. and Shin, J.Y. (2014). Actuator fault diagnosis in a Boeing 747 model via adaptive modified two-stage Kalman filter, International Journal of Aerospace Engineering 2014: 1–10.
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- [13] Hajiyev, C. (2013). Two-stage Kalman filter-based actuator/surface fault identification and reconfigurable control applied to F-16 fighter dynamics, International Journal of Adaptive Control and Signal Processing 27(9): 755–770.
- [14] Hassanabadi, A.H., Shafiee, M. and Puig, V. (2016). Robust fault detection of singular LPV systems with multiple time-varying delays, International Journal of Applied Mathematics and Computer Science 26(1): 45–61, DOI: 10.1515/amcs-2016-0004.
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- [16] Khan, A.Q., Abid, M. and Ding, S.X. (2014). Fault detection filter design for discrete-time nonlinear systems—A mixed H−/H∞ optimization, Systems & Control Letters 67(1): 46–54.
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- [19] Li, X., Liu, H.H.T. and Jiang, B. (2015). Parametrization of optimal fault detection filters, Automatica 56: 70–77.
- [20] Lu, P., Eykeren, L.V., Kampen, E.V. and Chu, Q.P. (2015). Selective-reinitialization multiple-model adaptive estimation for fault detection and diagnosis, Journal of Guidance, Control, and Dynamics 38(8): 1409–1424.
- [21] Lu, P., Kampen, E.V., Visser, C.D. and Chu, Q.P. (2016). Nonlinear aircraft sensor fault reconstruction in the presence of disturbances validated by realflight data, Control Engineering Practice 49: 112–128.
- [22] Mangoubi, R.S. (1998). Robust Estimation and Failure Detection: A Concise Treatment, Springer, London.
- [23] Mulgund, S.S. and Stengels, R.F. (1996). Optimal nonlinear estimation for aircraft flight control in wind shear, Automatica 32(1): 3–13.
- [24] Nicotra, M.M., Naldi, R. and Garone, E. (2017). Nonlinear control of a tethered UAV: The taut cable case, Automatica 78: 174–184.
- [25] Péni, T., Vanek, B., Szabó, Z. and Bokor, J. (2015). Supervisory fault tolerant control of the GTM UAV using LPV methods, International Journal of Applied Mathematics and Computer Science 25(1): 117–131, DOI: 10.1515/amcs-2015-0009.
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- [28] Rosa, P. and Silvestre, C. (2013). Fault detection and isolation of LPV systems using set-valued observers: An application to a fixed-wing aircraft, Control Engineering Practice 21(3): 242–252.
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- [31] Wu, C., Qi, J., Song, D., Qi, X. and Han, J. (2015). Simultaneous state and parameter estimation based actuator fault detection and diagnosis for an unmanned helicopter, International Journal of Applied Mathematics and Computer Science 25(1): 175–187, DOI: 10.1515/amcs-2015-0013.
- [32] Xu, F., Puig, V., Ocampo-Martinez, C., Olaru, S. and Niculescu, S.-I. (2017). Robust MPC for actuator fault tolerance using set-based passive fault detection and active fault isolation, International Journal of Applied Mathematics and Computer Science 27(1): 43–61, DOI: 10.1515/amcs-2017-0004.
- [33] Zhao, S. and Huang, B. (2017). Iterative residual generator for fault detection with linear time-invariant state-space models, IEEE Transactions on Automatic Control 62(10): 5422–5428.
- [34] Zhong, M., Ding, S.X. and Ding, E.L. (2010). Optimal fault detection for linear discrete time-varying systems, Automatica 46(8): 1395–1400.
- [35] Zhong, M., Ding, S.X. and Zhou, D. (2016a). A new scheme of fault detection for linear discrete time-varying systems, IEEE Transactions on Automatic Control 61(9): 2597–2602.
- [36] Zhong, M., Guo, J., Guo, D. and Yang, Z. (2016b). An extended Hi/H∞ optimization approach to fault detection of INS/GPS-Integrated system, IEEE Transactions on Instrumentation and Measurement 65(11): 2495–2504.
- [37] Zhong, M., Liu, H. and Song, N.F. (2015). On designing an extended H−/H∞-FDF for a class of nonlinear systems, Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015, Paris, France, pp. 707–712.
- [38] Zhong, M., Zhang, L., Ding, S.X. and Zhou, D. (2017). A probabilistic approach to robust fault detection for a class of nonlinear systems, IEEE Transactions on Industrial Electronics 64(5): 3930–3939.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-46489c84-b10f-47bc-8b96-195c047c4f88