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Fault tolerant multicontrollers for nonlinear systems: A real validation on a chemical process

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Języki publikacji
EN
Abstrakty
EN
An active sensor fault tolerant controller for nonlinear systems represented by a decoupled multimodel is proposed. Active fault tolerant control requires accurate fault estimation. Thus, to estimate both state variables and sensor faults, a discrete unknown input multiobserver, based on an augmented state multimodel, is designed. The multiobserver gains are computed by solving linear matrix inequalities with equality constraints. A multicontrol strategy is proposed for the compensation of the sensor fault and recovering the desired performances. This strategy integrates a bank of controllers, corresponding to a set of partial models, to generate a set of control laws compensating the fault effect. Then, a switching strategy between the generated local control laws is established in order to apply the most suitable control law that tolerates the fault and maintains good closed loop performances. The effectiveness of the proposed strategy is proven through a numerical example and also through a real time application on a chemical reactor. The obtained results confirm satisfactory closed loop performance in terms of trajectory tracking and fault tolerance.
Rocznik
Strony
61--74
Opis fizyczny
Bibliogr. 38 poz., rys., wykr.
Twórcy
  • Laboratory of Numerical Control of Industrial Processes, University of Gabes, Omar Ibn Khattab St., 6029, Gabes, Tunisia
  • Laboratory of Numerical Control of Industrial Processes, University of Gabes, Omar Ibn Khattab St., 6029, Gabes, Tunisia
  • Laboratory of Numerical Control of Industrial Processes, University of Gabes, Omar Ibn Khattab St., 6029, Gabes, Tunisia
Bibliografia
  • [1] Abdelkrim, N., Tellili, A. and Abdelkrim, M.N. (2012). Additive fault tolerant control applied to delayed singularly perturbed system, Journal of Software Engineering and Applications 5(04): 217.
  • [2] Allaoui, M., Messaoud, A., Dehri, K. and Ben Abdennour, R. (2017). Multimodel repetitive-predictive control of nonlinear systems: Rejection of unknown non-stationary sinusoidal disturbances, International Journal of Control 90(7): 1478–1494.
  • [3] Ben Atia, S., Messaoud, A. and Ben Abdennour, R. (2015). Decoupled multimodel predictive control based on multi-observer for discrete-time uncertain nonlinear systems, 12th International Multi-Conference on Systems, Signals and Devices (SSD), Mahdia, Tunisia, pp. 1–8.
  • [4] Ben Atia, S., Messaoud, A. and Ben Abdennour, R. (2018). An online identification algorithm of unknown time-varying delay and internal multimodel control for discrete non-linear systems, Mathematical and Computer Modelling of Dynamical Systems 24(1): 26–43.
  • [5] Ben Atia, S., Messaoud, A., Ltaief, M. and Ben Abdennour, R. (2014). Synthesis of multi-observers for discrete-time nonlinear systems with delayed output, International Journal of Sciences and Techniques of Automatic Control and Computer Engineering 8(1): 1966–1981.
  • [6] Bonfè, M., Castaldi, P., Mimmo, N. and Simani, S. (2011). Active fault tolerant control of nonlinear systems: The cart-pole example, International Journal of Applied Mathematics and Computer Science 21(3): 441–445, DOI: 10.2478/v10006-011-0033-y.
  • [7] Buciakowski, M., Witczak, M., Puig, V., Rotondo, D., Nejjari, F. and Korbicz, J. (2017). A bounded-error approach to simultaneous state and actuator fault estimation for a class of nonlinear systems, Journal of Process Control 52: 14–25.
  • [8] Chaves, E.R., André, F.A. and Maitelli, A.L. (2019). Robust observer-based actuator and sensor fault estimation for discrete-time systems, Journal of Control, Automation and Electrical Systems 30(2): 160–169.
  • [9] Du, D., Jiang, B. and Shi, P. (2014). Sensor fault estimation and accommodation for discrete-time switched linear systems, IET Control Theory and Applications 8(11): 960–967.
  • [10] Gao, Z., Cecati, C. and Ding, S.X. (2015). A survey of fault diagnosis and fault-tolerant techniques I: Fault diagnosis with model-based and signal-based approaches, IEEE Transactions on Industrial Electronics 62(6): 3757–3767.
  • [11] Ichalal, D., Marx, B., Ragot, J., Mammar, S. and Maquin, D. (2016). Sensor fault tolerant control of nonlinear Takagi–Sugeno systems. Application to vehicle lateral dynamics, International Journal of Robust and Nonlinear Control 26(7): 1376–1394.
  • [12] Jiang, J. and Yu, X. (2012). Fault-tolerant control systems: A comparative study between active and passive approaches, Annual Reviews in Control 36(1): 60–72.
  • [13] Jiang, Y. and Yin, S. (2018). Recent advances in key-performance-indicator oriented prognosis and diagnosis with a Matlab toolbox: DB-KIT, IEEE Transactions on Industrial Informatics 15(5): 2849–2858, DOI: 10.1109/TII.2018.2875067.
  • [14] Jiang, Y., Yin, S. and Kaynak, O. (2018). Data-driven monitoring and safety control of industrial cyber-physical systems: Basics and beyond, IEEE Access 6: 47374–47384.
  • [15] Khelassi, A., Theilliol, D., Weber, P. and Ponsart, J.C. (2011). Fault-tolerant control design with respect to actuator health degradation: An LMI approach, IEEE International Conference on Control Applications, Denver, CO, USA, pp. 983–988.
  • [16] Ma, F. and Hanna, M.A. (1999). Biodiesel production: A review, Bioresource Technology 70(1): 1–15.
  • [17] Meher, L., Vidya S., D. and Naik, S. (2006). Technical aspects of biodiesel production by transesterification—A review, Renewable and Sustainable Energy Reviews 10(3): 248–268.
  • [18] Messaoud, A. and Ben Abdennour, R. (2018). An experimental validation of a new method for multimodel identification, Journal of Dynamic Systems, Measurement, and Control 140(2): 024502.
  • [19] Messaoud, A., Ltaief, M. and Ben Abdennour, R. (2009). Supervision based on partial predictors for a multimodel generalised predictive control: Experimental validation on a semi-batch reactor, International Journal of Modelling, Identification and Control 6(4): 333–340.
  • [20] Narendra, K.S. and Balakrishnan, J. (1997). Adaptive control using multiple models, IEEE Transactions on Automatic Control 42(2): 171–187.
  • [21] Noura, H., Theilliol, D., Ponsart, J.C. and Chamseddine, A. (2009). Fault-Tolerant Control Systems: Design and Practical applications, Springer Science & Business Media, Dordrecht.
  • [22] Noura, H., Theilliol, D. and Sauter, D. (2000). Actuator fault-tolerant control design: Demonstration on a three-tank-system, International Journal of Systems Science 31(9): 1143–1155.
  • [23] Odgaard, P.F. and Stoustrup, J. (2012). Fault tolerant control of wind turbines using unknown input observers, 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Mexico City, Mexico, pp. 313–318.
  • [24] Orjuela, R., Marx, B., Ragot, J. and Maquin, D. (2009). On the simultaneous state and unknown input estimation of complex systems via a multiple model strategy, IET Control Theory & Applications 3(7): 877–890.
  • [25] Orjuela, R., Marx, B., Ragot, J. and Maquin, D. (2013). Nonlinear system identification using heterogeneous multiple models, International Journal of Applied Mathematics and Computer Science 23(1): 103–115, DOI: 10.2478/amcs-2013-0009.
  • [26] Pazera, M., Buciakowski, M. and Witczak, M. (2017). Robust multiple sensor fault-tolerant control for dynamic non-linear systems: Application to the aerodynamical twin-rotor system, International Journal of Applied Mathematics and Computer Science 28(2): 297–308, DOI: 10.2478/amcs-2018-0021.
  • [27] Pico, M.F. and Adam, E.J. (2017). Fault diagnosis and tolerant control using observer banks applied to continuous stirred tank reactor, Advances in Science, Technology and Engineering Systems Journal 1(3): 171–181.
  • [28] Prajapati, A.K. and Roy, B. (2016). Multi-fault diagnosis in three coupled tank system using unknown input observer, IFAC-PapersOnLine 49(1): 47–52.
  • [29] Rodrigues, M., Hamdi, H., Benhadj Braiek, N. and Theilliol, D. (2014). Observer-based fault tolerant control design for a class of LPV descriptor systems, Journal of the Franklin Institute 351(6): 3104–3125.
  • [30] Sobhani, M.H. and Poshtan, J. (2012). Fault detection and isolation using unknown input observers with structured residual generation, International Journal of Instrumentation and Control Systems 2(2): 1–12.
  • [31] Sojoudi, S., Lavaei, J. and Murray, R.M. (2011). Fault-tolerant controller design with applications in power systems and synthetic biology, American Control Conference (ACC), San Francisco, CA, USA, pp. 4135–4142.
  • [32] Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics 15(1): 116–132.
  • [33] Tanaka, K. and Wang, H.D. (2001). Fuzzy Control System Design and Analysis: A Linear Matrix Inequality Approach, Wiley, New York, NY.
  • [34] Theilliol, D., Join, C. and Zhang, Y. (2008). Actuator fault tolerant control design based on a reconfigurable reference input, International Journal of Applied Mathematics and Computer Science 18(4): 553–560, DOI: 10.2478/v10006-008-0048-1.
  • [35] Theilliol, D., Ponsart, J.C. and Noura, H. (2000). Sensor fault diagnosis and accommodation based on analytical redundancy: Application to a three-tank system, IFAC Proceedings Volumes 33(11): 535–540.
  • [36] Witczak, M., Buciakowski, M., Puig, V., Rotondo, D. and Nejjari, F. (2016). An LMI approach to robust fault estimation for a class of nonlinear systems, International Journal of Robust and Nonlinear Control 26(7): 1530–1548.
  • [37] Yu, X. and Jiang, J. (2015). A survey of fault-tolerant controllers based on safety-related issues, Annual Reviews in Control 39: 46–57.
  • [38] Zhang, Y. and Jiang, J. (2008). Bibliographical review on reconfigurable fault-tolerant control systems, Annual Reviews in Control 32(2): 229–252.
Uwagi
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Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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