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2012 | 22 | 1 | 225-237
Tytuł artykułu

Nonlinear model predictive control of a boiler unit: A fault tolerant control study

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.
Rocznik
Tom
22
Numer
1
Strony
225-237
Opis fizyczny
Daty
wydano
2012
otrzymano
2011-01-28
poprawiono
2011-07-04
Twórcy
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland
Bibliografia
  • Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. (2006). Diagnosis and Fault-Tolerant Control, Springer, Berlin.
  • Bonivento, C., Isidori, A., Marconi, L. and Paoli, A. (2004). Implicit fault-tolerant control: Application to induction motors, Automatica 40(3): 355-371.
  • Breger, L. and How, J.P. (2006). Nonlinear model predictive control technique for unmaned air vehicles, Journal of Guidance, Control and Dynamics 29(5): 1179-1188.
  • Camacho, E.F. and Bordóns, C. (2004). Model Predictive Control, Springer-Verlag, Berlin.
  • Chilin, D., Liu, J., de la Pẽna, D.M., Christofides, P.D. and Davis, J. F. (2010). Detection, isolation and handling of actuators faults in distributed model predictive control, Journal of Process Control 20(9): 1059-1075.
  • Clark, D.W., Mothadi, C. and Tuffs, P.S. (1987). Generalized predictive control-Part I: The basic algorithm, Automatica 23(2): 137-148.
  • Ducard, G.J.J. (2009). Fault-tolerant Flight Control and Guidance Systems. Practical Methods for Small Unmanned Aerial Vehicles, Advances in Industrial Control, Springer, London.
  • Haykin, S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Edn., Prentice-Hall, Upper Saddle River, NJ.
  • Isermann, R. (2006). Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance, Springer-Verlag, New York, NY.
  • Joosten, D.A. and Maciejowski, J. (2009). MPC design for faulttolerant flight control purposes based upon an existing output feedback controller, Proceedings of 7th International Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2009, Barcelona, Spain, (on CD-ROM).
  • Korbicz, J., Kościelny, J., Kowalczuk, Z. and Cholewa, W. (Eds.) (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer-Verlag, Berlin/Heidelberg.
  • Maciejowski, J.M. (1998). The implicit daisy-chaining property of constrained predictive control, Applied Mathematics and Computer Science 8(4): 695-711.
  • Maciejowski, J. (2002). Predictive Control with Constraints, Prentice-Hall, Harlow.
  • Mayne, D.Q., Rawlings, J.B., Rao, C.V. and Scokaert, P.O.M. (2000). Constrained model predictive control: Stability and optimality, Automatica 36(6): 789-814.
  • Nørgaard, M., Ravn, O., Poulsen, N. and Hansen, L. (2000). Networks for Modelling and Control of Dynamic Systems, Springer-Verlag, London.
  • Noura, K., Theilliol, D., Ponsart, J.C. and Chamseddine, A. (2009). Fault Tolerant Control Systems. Design and Practical Applications, Advanced in Industrial Control, Springer, London.
  • Patan, K. (2008). Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes, Lecture Notes in Control and Information Sciences, Vol. 377, Springer-Verlag, Berlin.
  • Patan, K. (2009a). Fault detection and accommodation by means of neural networks. application to the boiler unit, Proceedings of the 7th International Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2009, Barcelona, Spain, (on CD-ROM).
  • Patan, K. (2009b). Locally recurrent networks for fault approximation and accommodation, in Z. Kowalczuk (Ed.), Diagnosis of Processes and Systems, Pomeranian Science and Technology Publishers PWNT, Gdańsk, pp. 263-270.
  • Sourander, M., Vermasvuori, M., Sauter, D., Liikala, T. and Jämsä-Jounela, S.-L. (2009). Fault tolerant control for a dearomatisation process, Journal of Process Control 19(7): 1091-1102.
  • Staroswiecki, M., Yang, H. and Jiang, B. (2007). Active fault tolerant control based on progressive accommodation, Automatica 43(12): 2070-2076.
  • Theillol, D., Cédric, J. and Zhang, Y. (2008). Actuator fault tolerant control design based on reconfigurable reference input, International Journal of Applied Mathematics and Computer Science 18(4): 553-560, DOI: 10.2478/v10006008-0048-1.
  • Zhang, Y. (2007). Active fault-tolerant control systems: Integration of fault diagnosis and reconfigurable control, in J. Korbicz, K. Patan and M. Kowal (Eds.), Fault Diagnosis and Fault Tolerant Control, Challenging Problems of Science-Theory and Applications: Automatic Control and Robotics, Academic Publishing House EXIT, Warsaw, pp. 21-41.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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