PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A robust predictive actuator fault-tolerant control scheme for Takagi-Sugeno fuzzy systems

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper deals with the problem of robust predictive fault-tolerant control for nonlinear discrete-time systems described by the Takagi-Sugeno models. The proposed approach is based on a triple stage procedure, i.e. it starts from fault estimation while the fault is compensated with a robust controller. The robust controller is designed without taking into account the input constraints related with the actuator saturation that may change due to its faulty behaviour. Thus, to check the compensation feasibility, the robust invariant set is developed, which takes into account the input constraints. If the current state does not belong to the robust invariant set, then suitable predictive control actions are performed in order to enhance the invariant set. This appealing phenomenon makes it possible to enlarge the domain of attraction, which makes the proposed approach an efficient solution for the fault-tolerant control. The final part of the paper shows an illustrative example regarding the application of the proposed approach to the twin-rotor system.
Rocznik
Strony
977--987
Opis fizyczny
Bibliogr. 41 poz., rys.
Twórcy
autor
  • Institute of Control and Computation Engineering, University of Zielona Góra, 9 Licealna St., 65-417 Zielona Góra, Poland
autor
  • Institute of Control and Computation Engineering, University of Zielona Góra, 9 Licealna St., 65-417 Zielona Góra, Poland
  • M.Witczak@issi.uz.zgora.pl
autor
  • Institute of Control and Computation Engineering, University of Zielona Góra, 9 Licealna St., 65-417 Zielona Góra, Poland
autor
  • Universite de Lorraine, CRAN, UMR 7039, Campus Sciences, BP70239, Vandoeuvre-les-Nancy Cedex 54506, France
Bibliografia
  • [1] Y. Zhang and J. Jiang, “Bibliographical review on reconfigurable fault-tolerant control systems”, IFAC Symp. Fault Detection Supervision and Safety of Technical Processes, SAFEPROCESS 1, 265-276 (2003).
  • [2] S. de Oca, V. Puig, M. Witczak, and L. Dziekan, “Faulttolerant control strategy for actuator faults using LPV techniques: application to a two degree of freedom helicopter”, Int. J. Applied Mathematics and Computer Science 22 (1), 161-171 (2012).
  • [3] J. Korbicz, J. Kościelny, Z. Kowalczuk, and W. Cholewa, Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer-Verlag, Berlin, 2004.
  • [4] H. Li, Q. Zhao, and Z. Yang, “Reliability modeling of fault tolerant control systems”, Int. J. Applied Mathematics and Computer Science 17 (4), 491-504 (2007).
  • [5] M. Mrugalski, “An unscented Kalman filter in designing dynamic gmdh neural networks for robust fault detection”, Int. J. Applied Mathematics and Computer Science 23 (1), 157-169 (2013).
  • [6] M. Witczak, “Modelling and Estimation Strategies for Fault Diagnosis of Non-linear Systems”, Springer-Verlag, Berlin, 2007.
  • [7] W. Chen, A.Q. Khan, M. Abid, and S.X. Ding, “Integrated design of observer-based fault detection for a class of uncertain non-linear systems”, Int. J. Applied Mathematics and Computer Science 21 (4), 619-636 (2011).
  • [8] M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki, Diagnosis and Fault-Tolerant Control, Springer-Verlag, New York, 2003.
  • [9] M. Witczak, Fault Diagnosis and Fault-Tolerant Control Strategies for Non-linear Systems, ISBN: 978-3-319-03013-5, Lecture Notes in Electrical Engineering, Vol. 266, Springer International Publishing Switzerland, Cham, 2014.
  • [10] R. Isermann, Fault Diagnosis Applications: Model Based Condition Monitoring, Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems, Springer-Verlag, Berlin, 2011.
  • [11] M. Mahmoud, J. Jiang, and Y. Zhang, Active Fault Tolerant Control Systems: Stochastic Analysis and Synthesis, Springer- Verlag, Berlin, 2003.
  • [12] J. Korbicz, M. Witczak, and V. Puig, “LMI-based strategies for designing observers and unknown input observers for nonlinear discrete-time systems”, Bull. Pol. Ac.: Tech. 55 (1), 31-42 (2007).
  • [13] M. Witczak and P. Pretki, “Design of an extended unknown input observer with stochastic robustness techniques and evolutionary algorithms”, Int. J. Control 80 (5), 749-762 (2007).
  • [14] H. Noura, D. Theilliol, J. Ponsart, and A. Chamseddine, Faulttolerant Control Systems: Design and Practical Applications, Springer-Verlag, Berlin, 2003.
  • [15] M. Witczak, V. Puig, and S. de Oca, “A fault-tolerant control strategy for non-linear discrete-time systems: application to the twin-rotor system”, Int. J. Control 86 (10), 1788-1799 (2013).
  • [16] B. Kouvaritakis, J.A. Rossiter, and J. Schuurmans, “Efficient robust predictive control”, IEEE Trans. Automatic Control 45 (8), 1545-1549 (2000).
  • [17] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control”, IEEE Trans. Systems, Man and Cybernetics 15 (1), 116-132 (1985).
  • [18] K. Tanaka and H.O. Wang, Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach, Wiley- Interscience, New York, 2001.
  • [19] M. Chadli and H.R. Karimi, “Robust observer design for unknown inputs Takagi-Sugeno models”, IEEE Trans. on Fuzzy Systems 21 (1), 158-164 (2013).
  • [20] D. Ichalal, B. Marx, J. Ragot, and D. Maquin, “Advances in observer design for takagi-sugeno systems with unmeasurable premise variables”, 20th IEEE Mediterranean Conf. on Control & Automation (MED) 1, 848-853 (2012).
  • [21] S. Gillijns and B. De Moor, “Unbiased minimum-variance input and state estimation for linear discrete-time systems”, Automatica 43, 111-116 (2007).
  • [22] B. Kolodziejczak and T. Szulc, “Convex combinations of matrices-full rank characterization”, Linear Algebra and Its Applications 287 (1-3), 215-222 (1999).
  • [23] M.C. de Oliveira, J. Bernussou, and J.C. Geromel, “A new discrete-time robust stability condition”, Systems and Control Letters 37 (4), 261-265 (1999).
  • [24] J. Daafouz, P. Riedinger, and C. Iung, “Stability analysis and control synthesis for switched systems: a switched lyapunov function approach”, IEEE Trans. Automatic Control 47 (11), 1883-1887 (2002).
  • [25] F. Delmotte, T.M. Guerra, and M. Ksantini, “Continuous takagi-sugeno’s models: reduction of the number of lmi conditions in various fuzzy control design technics”, IEEE Trans. Fuzzy Systems 15 (3), 426-438 (2007).
  • [26] H. Li and M. Fu, “A linear matrix inequality approach to robust h1 filtering”, IEEE Trans. Signal Processing 45 (9), 2338-2350 (1997).
  • [27] A. Zemouche, M. Boutayeb, and G. Iulia Bara, “Observer for a class of Lipschitz systems with extension to H1 performance analysis”, Systems and Control Letters 57 (1), 18-27 (2008).
  • [28] H.O. Wang, K. Tanaka, and M.F. Griffin, “An approach to fuzzy control of nonlinear systems: stability and design issues”, IEEE Trans. Fuzzy Systems 4 (1), 14-23 (1996).
  • [29] T.M. Guerra, A. Kruszewski, and J. Lauber, “Discrete tagaki- sugeno models for control: Where are we?”, Annual Reviews in Control 33 (1), 37-47 (2009).
  • [30] H.D. Tuan, P. Apkarian, T. Narikiyo, and Y. Yamamoto, “Parameterized linear matrix inequality techniques in fuzzy control system design”, IEEE Trans. Fuzzy Systems 9 (2), 324-332 (2001).
  • [31] F. Blanchini, “Set invariance in control”, Automatica 35, 1747-1767 (1999).
  • [32] B. Kouvaritakis, Y.I. Lee, and M. Cannon, “Extended invariance and its use in model predictive control”, Automatica 41 (2005).
  • [33] L. Zongli and L. Liang, “Set invariance conditions for singular linear systems subject to actuator saturation”, IEEE Trans. Automatic Control 52 (12), 2351-2355 (2007).
  • [34] A. Alessandri, M. Baglietto, and G. Battistelli, “Design of state estimators for uncertain linear systems using quadratic boundedness”, Automatica 42 (3), 497-502 (2006).
  • [35] T. Zou and S. Li, “Stabilization via extended nonquadratic boundedness for constrained nonlinear systems in Takagi- Sugeno’s form”, J. Franklin Institute 348 (10), 2849-2862 (2011).
  • [36] E.G. Gilbert and K.T. Tan, “Linear systems with state and control constraints: The theory and application of maximal output admissible sets”, IEEE Trans. Automatic Control 36 (9), 1008-1020 (1991).
  • [37] K. Derinkuyu and M. Pınar, “On the s-procedure and some variants”, Mathematical Methods of Operations Research 64 (1), 55-77 (2006).
  • [38] L. Imsland, N. Bar, and B.A. Foss, “More efficient predictive control”, Automatica 41 (8), 1395-1403, (2005).
  • [39] B. Kouvaritakis, M. Cannon, and J.A. Rossiter, “Who needs QP for linear MPC anyway?”, Automatica 38 (5), 879-884 (2002).
  • [40] Feedback Instruments Limited, Twin Rotor MIMO System Advanced Teaching Manual 1, Academic Press, Crowborough, 1998.
  • [41] A. Rahideh and M. H. Shaheed, “Mathematical dynamic modelling of a twin-rotor multiple input-multiple output system”, Institution of Mechanical Engineers, Part I: J. Systems and Control Engineering 227, 89-101 (2007).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-205f0b6d-328a-4b9c-8420-0175a3cbf745
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.