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Fault identification in underwater vehicle thrusters via sliding mode observers

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper is devoted to the problem of increasing the efficiency of underwater vehicles by using a fault diagnosis system for their thrusters which provides detection, isolation, and identification of minor faults. To address the problem, a two-stage method is proposed. At the first stage, a bank of diagnostic observers is designed to detect and isolate the emerging faults. Each observer in this bank is constructed to be sensitive to some set of faults and insensitive to others. At the second stage, additional observers working in sliding mode are synthesized in order to accurately estimate the error value in the signal obtained from the angular velocity sensor and to estimate deviations of the thruster parameters from their nominal values due to the faults. In contrast to the existing solutions, reduced-order (i.e., lower-dimensional) models of the original system are proposed as a basis to construct sliding mode observers. This approach permits reduction of the complexity of the obtained observers in comparison with the known methods, where full-order observers are constructed. The simulation results show the efficiency and high quality of all synthesized observers. In all cases considered, it was possible to detect typical faults, as well as estimate their values.
Rocznik
Strony
679--688
Opis fizyczny
Bibliogr. 34 poz., rys., wykr.
Twórcy
  • Institute of Automation and Control Processes, Russian Academy of Sciences—Far Eastern Branch, 5 Radio Street, Vladivostok, 690041, Russia; Institute for Marine Technology Problems, Russian Academy of Sciences—Far Eastern Branch, 5 Sukhanova Street, Vladivostok, 690091, Russia
  • Department of Automation and Control, Far Eastern Federal University, 8 Sukhanova Street, Vladivostok, 690091, Russia; Institute for Marine Technology Problems, Russian Academy of Sciences—Far Eastern Branch, 5 Sukhanova Street, Vladivostok, 690091, Russia
  • Institute of Automation and Control Processes, Russian Academy of Sciences—Far Eastern Branch, 5 Radio Street, Vladivostok, 690041, Russia
Bibliografia
  • [1] Alwi, H. and Edwards, C. (2008). Fault tolerant control using sliding modes with on-line control allocation, Automatica 44(7): 1859–1866, DOI: 10.1016/j.automatica.2007.10.034.
  • [2] Bartoszewicz, A. and Adamiak, K. (2019). A reference trajectory based discrete time sliding mode control strategy, International Journal of Applied Mathematics and Computer Science 29(3): 517–525, DOI: 10.1515/amcs-2019-0038.
  • [3] Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. (2006). Diagnosis and Fault-Tolerant Control, Springer, Berlin.
  • [4] Byrski, W., Drapała, M. and Byrski, J. (2019). An adaptive identification method based on the modulating functions technique and exact state observers for modeling and simulation of a nonlinear MISO glass melting process, International Journal of Applied Mathematics and Computer Science 29(4): 739–757, DOI: 10.2478/amcs-2019-0055.
  • [5] Chirikjian, G. (2009). Robotic self-replication, self-diagnosis, and self-repair: Probabilistic considerations, in H. Asama et al. (Eds), Distributed Autonomous Robotic Systems 8, Springer, Berlin/Heiderberg, pp. 273–281, DOI: 10.1007/978-3-642-00644-9 24.
  • [6] Daidola, J. and Johnson, F. (1992). Propeller Selection and Optimization Program, Manual for the Society of Naval Architects and Marine, New York, NY.
  • [7] Davila, J., Fridman, L. and Poznyak, A. (2006). Observation and identification of mechanical systems via second order sliding modes, International Journal of Control 79(10): 1251–1262, DOI: 10.1080/00207170600801635.
  • [8] Edwards, C., Alwi, H. and Tan, C.P. (2012). Sliding mode methods for fault detection and fault tolerant control with application to aerospace systems, International Journal of Applied Mathematics and Computer Science 22(1): 109–124, DOI: 10.2478/v10006-012-0008-7.
  • [9] Edwards, C. and Spurgeon, S. (1994). On the development of discontinuous observers, International Journal of Control 59(5): 1211–1229, DOI: 10.1080/00207179408923128.
  • [10] Edwards, C., Spurgeon, S. and Patton, R. (2000). Sliding mode observers for fault detection and isolation, Automatica 36(4): 541–553, DOI: 10.1016/S0005-1098(99)00177-6.
  • [11] Escobet, T., Bregon, A., Pulido, B. and Puig, V. (2019). Fault Diagnosis of Dynamic Systems, Springer, Berlin.
  • [12] Filaretov, V., Zhirabok, A., Zuev, A. and Protcenko, A. (2012). The development of the faults accommodation system for actuators of multilink manipulators, Proceedings of the 23rd DAAAM International Symposium on Intelligent Manufacturing and Automation, Vienna, Austria, pp. 575–578.
  • [13] Fridman, L., Levant, A. and Davila, J. (2007). Observation of linear systems with unknown inputs via high-order sliding modes, International Journal of Systems Science 38(10): 773–791, DOI: 10.1080/00207720701409538.
  • [14] Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York, NY.
  • [15] He, J. and Zhang, C. (2012). Fault reconstruction based on sliding mode observer for nonlinear systems, Mathematical Problems in Engineering 2012(2): 1–22, DOI: 10.1155/2012/451843.
  • [16] Inzarcev, A., Kiselev, L. and Kostenko, V. (2018). Underwater Robotics: Systems, Technologies, Application, IMTP FEB RAS, Vladivostok, (in Russian).
  • [17] Kalsi, K., Hui, S. and Zak, S. (2011). Unknown input and sensor fault estimation using sliding-mode observers, Proceedings of the American Control Conference, San Francisco, CA, USA, pp. 1364–1369.
  • [18] Mironovsky, L. (1998). Functional Diagnosis of Dynamic Systems, Nauka, Moscow, (in Russian).
  • [19] Pisarets, A., Zhirabok, A. and Inzartsev, A. (2004). On diagnosis for thrusters of underwater vehicles, Proceedings of the Sixth ISOPE Pacific/Asia Offshore Mechanics Symposium, Vladivostok, Russia, pp. 255–259.
  • [20] Rascón, R., Rosas, D. and Hernandez-Balbuena, D. (2017). Regulation control of an underactuated mechanical system with discontinuous friction and backlash, International Journal of Applied Mathematics and Computer Science 27(4): 785–797, DOI: 10.1515/amcs-2017-0055.
  • [21] Sarkar, N., Podder, T. and Antonelli, G. (2002). Fault-accommodating thruster force allocation of an AUV considering thruster redundancy and saturation, IEEE Transactions on Robotics and Automation 18(2): 223–233, DOI: 10.1109/TRA.2002.999650.
  • [22] Simani, S., Fantuzzi, C. and Patton, R. (2002). Model-based Fault Diagnosis in Dynamic Systems Using Identification, Springer, Berlin.
  • [23] Tan, C. and Edwards, C. (2003). Sliding mode observers for robust detection and reconstruction of actuator and sensor faults, International Journal of Robust and Nonlinear Control, 13(5): 443–463, DOI: 10.1002/rnc.723.
  • [24] Utkin, V. (1992). Sliding Modes in Control Optimization, Springer, Berlin.
  • [25] Wang, J. (2012a). Fault diagnosis of underwater vehicle with FNN, Proceedings of the 10th World Congress on Intelligent Control and Automation, Beijing, China, pp. 2931–2934, DOI: 10.1109/WCICA.2012.6358371.
  • [26] Wang, J. (2012b). Fault diagnosis of underwater vehicle with neural network, Proceedings of the 24th Chinese Control and Decision Conference (CCDC), Taiyuan, China, pp. 1613–1617, DOI: 10.1109/CCDC.2012.6243012.
  • [27] Wang, J., Wu, G., Wan, L., Sun, Y. and Jiang, D. (2009). Recurrent neural network applied to fault diagnosis of underwater robots, Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems, Shanghai, China, pp. 593–598, DOI: 10.1109/ICICISYS.2009.5357773.
  • [28] Zhang, M., Wu, J. and Wang, Y. (2011). Simultaneous faults detection and location of thrusters and sensors for autonomous underwater vehicle, Proceedings of the 4th International Conference on Intelligent Computation Technology and Automation, Shenzhen, China, pp. 504–507. DOI: 10.1109/ICICTA.2011.139.
  • [29] Zhao, B., Skjetne, R., Blanke, M. and Dukan, F. (2014). Particle filter for fault diagnosis and robust navigation of underwater robot, IEEE Transactions on Control Systems Technology 22(6): 2399–2407, DOI: 10.1109/TCST.2014.2300815.
  • [30] Zhirabok, A., Shumsky, A., Solyanik, S. and Suvorov, A. (2017). Fault detection in nonlinear systems via linear methods, International Journal of Applied Mathematics and Computer Science 27(2): 261–272, DOI: 10.1515/amcs-2017-0019.
  • [31] Zhirabok, A., Zuev, A. and Shumsky, A. (2019). Diagnosis of linear systems based on sliding mode observers, Journal of Computer and Systems Sciences International 58(6): 898–914, DOI: 10.1134/S1064230719040166.
  • [32] Zhirabok, A., Zuev, A. and Shumsky, A. (2020a). Diagnosis of linear dynamic systems: An approach based on sliding mode observers, Automation and Remote Control 81(2): 345–358, DOI: 10.1134/S0005117920020022.
  • [33] Zhirabok, A., Zuev, A. and Shumsky, A. (2020b). Identification of faults in the sensors of technical systems with the use of sliding mode observers, Measurement Techniques 62(10): 869–878, DOI: 10.1007/s11018-020-01707-1.
  • [34] Zhu, D. and Sun, B. (2013). Information fusion fault diagnosis method for unmanned underwater vehicle thrusters, IET Electrical Systems in Transportation 3(4): 102–111, DOI: 10.1049/iet-est.2012.0052.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f28bde9a-ba46-4c69-a7f5-69561e915156
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