Powiadomienia systemowe
- Sesja wygasła!
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The paper deals with the problem of designing sensor-fault tolerant control for a class of non-linear systems. The scheme is composed of a robust state and fault estimator as well as a controller. The estimator aims at recovering the real system state irrespective of sensor faults. Subsequently, the fault-free state is used for control purposes. Also, the robust sensor fault estimator is developed in a such a way that a level of disturbances attenuation can be reached pertaining to the fault estimation error. Fault-tolerant control is designed using similar criteria. Moreover, a separation principle is proposed, which makes it possible to design the fault estimator and control separately. The final part of the paper is devoted to the comprehensive experimental study related to the application of the proposed approach to a non-linear twin-rotor system, which clearly exhibits the performance of the new strategy.
Rocznik
Tom
Strony
297--308
Opis fizyczny
Bibliogr. 32 poz., rys., wykr.
Twórcy
autor
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland
autor
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland
autor
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland
Bibliografia
- [1] Aouaouda, S. Chadli, M., Shi, P. and Karimi, H. (2015). Discrete-time H /H-inf sensor fault detection observer design for nonlinear systems with parameter uncertainty, International Journal of Robust and Nonlinear Control 25(3): 339–361.
- [2] Byrski, J. and Byrski, W. (2016). A double window state observer for detection and isolation of abrupt changes in parameters, International Journal of Applied Mathematics and Computer Science 26(3): 585–602, DOI: 10.1515/amcs-2016-0041.
- [3] Cai, J., Ferdowsi, H. and Sarangapani, J. (2016). Model-based fault detection, estimation, and prediction for a class of linear distributed parameter systems, Automatica 66: 122–131.
- [4] de Oliveira, M.C., Bernussou, J. and Geromel, J.C. (1999). A new discrete-time robust stability condition, Systems & Control Letters 37(4): 261–265.
- [5] Defoort, M., Veluvolu, K. and Rath, J.and Djemai, M. (2016). Adaptive sensor and actuator fault estimation for a class of uncertain Lipschitz nonlinear systems, International Journal of Adaptive Control and Signal Processing 30(2): 271–283.
- [6] Foo, G., Zhang, X. and Vilathgamuwa, M. (2013). A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter, IEEE Transactions on Industrial Electronics 60(8): 3485–3495.
- [7] Ha, Q.P. and Trinh, H. (2004). State and input simultaneous estimation for a class of nonlinear systems, Automatica 40(10): 1779–1785.
- [8] He, Q. and Liu, J. (2014). Sliding mode observer for a class of globally Lipschitz non-linear systems with time-varying delay and noise in its output, IET Control Theory & Applications 8(14): 1328–1336.
- [9] Isermann, R. (2011). Fault-Diagnosis Applications. Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems, Springer, Berlin/Heidelberg.
- [10] Khalil, H. and Praly, L. (2014). High-gain observers in nonlinear feedback control, International Journal of Robust and Nonlinear Control 24(6): 993–1015.
- [11] Li, H. and Fu, M. (1997). A linear matrix inequality approach to robust H∞ filtering, IEEE Transactions on Signal Processing 45(9): 2338–2350.
- [12] Li, L., Yang, Y., Zhang, Y. and Ding, S. (2014). Fault estimation of one-sided Lipschitz and quasi-one-sided Lipschitz systems, 33rd Chinese Control Conference (CCC), Nanjing, China, pp. 2574–2579.
- [13] Löfberg, J. (2004). YALMIP: A toolbox for modeling and optimization in Matlab, Proceedings of the CACSD Conference, New Orleans, LA, USA, pp. 284–289.
- [14] López-Estrada, F.-R., Ponsart, J.-C., Theilliol, D., Astorga-Zaragoza, C.-M., and Camas-Anzueto, J.-L. (2015). Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor, International Journal of Applied Mathematics and Computer Science 25(2): 233–244, DOI: 10.1515/amcs-2015-0018.
- [15] Mahmoud, M., Jiang, J. and Zhang, Y. (2003). Active Fault Tolerant Control Systems: Stochastic Analysis and Synthesis, Springer, Berlin/Heidelberg.
- [16] Majdzik, P., Akielaszek-Witczak, A., Seybold, L., Stetter, R. and Mrugalska, B. (2016). A fault-tolerant approach to the control of a battery assembly system, Control Engineering Practice 55: 139–148.
- [17] Mrugalski, M. (2014). Advanced Neural Network-based Computational Schemes for Robust Fault Diagnosis, Springer, Berlin/Heidelberg.
- [18] Nguyen, M.C. and Trinh, H. (2016a). Reduced-order observer design for one-sided Lipschitz time-delay systems subject to unknown inputs, IET Control Theory & Applications 10(10): 1097–1105.
- [19] Nguyen, M.C. and Trinh, H. (2016b). Unknown input observer design for one-sided Lipschitz discrete-time systems subject to time-delay, Applied Mathematics and Computation 286: 57–71.
- [20] Pourbabaee, B., Meskin, N. and Khorasani, K. (2016). Sensor fault detection, isolation, and identification using multiple-model-based hybrid Kalman filter for gas turbine engines, IEEE Transactions on Control Systems Technology 24(4): 1184–1200.
- [21] Rotondo, D., Nejjari, F. and Puig, V. (2013). Quasi-LPV modeling, identification and control of a twin rotor MIMO system, Control Engineering Practice 21(6): 829–846.
- [22] Seron, M.M. and De Doná, J.A. (2015). Robust fault estimation and compensation for LPV systems under actuator and sensor faults, Automatica 52: 294–301.
- [23] Seybold, L., Witczak, M., Majdzik, P. and Stetter, R. (2015). Towards robust predictive fault-tolerant control for a battery assembly system, International Journal of Applied Mathematics and Computer Science 25(4): 849–862, DOI: 10.1515/amcs-2015-0061.
- [24] Song, J. and He, S. (2015). Robust finite-time H-inf control for one-sided Lipschitz nonlinear systems via state feedback and output feedback, Journal of the Franklin Institute 352(8): 3250–3266.
- [25] Tabatabaeipour, S.M. and Bak, T. (2014). Robust observer-based fault estimation and accommodation of discrete-time piecewise linear systems, Journal of the Franklin Institute 351(1): 277–295.
- [26] Witczak, M. (2014). Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems: Analytical and Soft Computing Approaches, Springer, Heidelberg.
- [27] Witczak, M., Buciakowski, M., Puig, V., Rotondo, D. and Nejjari, F. (2015). An LMI approach to robust fault estimation for a class of nonlinear systems, International Journal of Robust and Nonlinear Control 26(7): 1530–1548.
- [28] Zemouche, A. and Boutayeb, M. (2006). Observer design for Lipschitz non-linear systems: The discrete time case, IEEE Transactions on Circuits and Systems 53(8): 777–781.
- [29] Zemouche, A., Boutayeb, M. and Bara, G.I. (2008). Observers for a class of Lipschitz systems with extension to H∞ performance analysis, Systems & Control Letters 57(1): 18–27.
- [30] Zhang, J., Swain, A. and Nguang, S. (2014a). Robust H-inf adaptive descriptor observer design for fault estimation of uncertain nonlinear systems, Journal of the Franklin Institute 351(11): 5162–5181.
- [31] Zhang,W., Su, H., Su, S. and Wang, D. (2014b). Nonlinear H-inf observer design for one-sided Lipschitz systems, Neurocomputing 145: 505–511.
- [32] Zhang, W., Su, H., Zhu, F. and Azar, G. (2015). Unknown input observer design for one-sided Lipschitz nonlinear systems, Nonlinear Dynamics 79(2): 1469–1479.
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-61915466-fdad-40cc-aba1-f9d644a32d1d