Abstract
The paper deals with the problem of neural-network based on robust unknown input observer design for the fault diagnosis. Authors review the recent development in the area of robust observers for non-linear discrete-time systems and propose less restrictive procedure for design of the \({\mathcal {H}_\infty }\) observer. The approach guaranties simultaneously the unknown input decoupling and the fault estimation. The paper presents an unknown input observer design that reduces to a set of linear matrix inequalities. The final part of the paper presents an illustrative example devoted to fault diagnosis of the wind turbine.
The work was financed as a research project with the science funds with the kind support of the National Science Centre in Poland
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bendtsen, J.D., Trangbk, K.: Robust quasi-lpv control based on neural state space models. IEEE Transactions on Neural Networks, 355–368 (2002)
Bououden, S., Chadli, M., Filali, S., El Hajjaji, A.: Fuzzy model based multivariable predictive control of a variable speed wind turbine: \(\{\)LMI\(\}\) approach. Renewable Energy 37(1), 434–439 (2012)
Chadli, M., Karimi, H.R.: Robust observer design for unknown inputs takagi-sugeno models. IEEE Transactions on Fuzzy Systems 21(1), 158–164 (2013)
De Oca, S., Puig, V., Witczak, M., Dziekan, L.: Fault-tolerant control strategy for actuator faults using lpv techniques: application to a two degree of freedom helicopter. International Journal of Applied Mathematics and Computer Science 22(1), 161–171 (2012)
de Oliveira, M.C., Bernussou, J., Geromel, J.C.: A new discrete-time robust stability condition. Systems and Control Letters 37(4), 261–265 (1999)
Guerra, T.M., Kruszewski, A., Lauber, J.: Discrete Tagaki-Sugeno models for control: Where are we? Annual Reviews in Control 33(1), 37–47 (2009)
Iserman, R.: Fault Diagnosis Applications: Model Based Condition Monitoring, Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems. Springer-Verlag, Berlin (2011)
Lu, S., Basar, T.: Robust nonlinear system identification using neural-network models. IEEE Transactions on Neural Networks 9(3), 407–429 (1998)
Mrugalski, M.: An unscented kalman filter in designing dynamic gmdh neural networks for robust fault detection. International Journal of Applied Mathematics and Computer Science 23(1), 157–169 (2013)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Systems, Man and Cybernetics 15(1), 116–132 (1985)
Tuan, H.D., Apkarian, P., Narikiyo, T., Yamamoto, Y.: Parameterized linear matrix inequality techniques in fuzzy control system design. IEEE Transactions on Fuzzy Systems 9(2), 324–332 (2001)
Varga, A., Ossmann, D.: \(\{\)LPV\(\}\) model-based robust diagnosis of flight actuator faults. Control Engineering Practice 31, 135–147 (2014)
Veluvolu, K.C., Kim, M.Y., Lee, D.: Nonlinear sliding mode high-gain observers for fault estimation. International Journal of Systems Science 42(7), 1065–1074 (2011)
Wang, H.O., Tanaka, K., Griffin, M.F.: An approach to fuzzy control of nonlinear systems: stability and design issues. IEEE Transactions on Fuzzy Systems 4(1), 14–23 (1996)
Witczak, M.: Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems. Lectures Notes in Electrical Engineering, vol. 266. Springer International Publisher, Heidelberg (2014)
Witczak, M., Witczak, P.: Efficient predictive fault-tolerant control for non-linear systems. In: Korbicz, J., Kowal, M. (eds.) Intelligent Systems in Technical and Medical Diagnostics. AISC, vol. 230, pp. 65–76. Springer, Heidelberg (2013)
Witczak, M., Witczak, P., Korbicz, J., Aubrun, C.: Robust and efficient predictive ftc: application to wind turbines. In: Conference on Control and Fault-Tolerant Systems - SysTol 2013, Nice, France, pp. 371–376 (2013)
Witczak, P. Luzar, M., Witczak, M., Korbicz, J.: A robust fault-tolerant model predictive control for linear parameter-varying systems. In: Proceedings of Methods and Models in Automation and Robotics - MMAR, pp. 462–467 (2014)
Zemouche, A., Boutayeb, M., Bara, G.I.: Observer for a class of \(\text{ Lipschitz }\) systems with extension to \(\cal H_{\infty }\) performance analysis. Systems and Control Letters 57(1), 18–27 (2008)
Polycarpou, M.M., Prisini, T., Zhang, X.: Fault diagnosis of a class of nonlinear uncertain systems with lipschitz nonlinearities using adaptive estimation. Automatica 46(2), 290–299 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Witczak, P., Mrugalski, M., Patan, K., Witczak, M. (2015). A Neural-Network-Based Robust Observer for Simultaneous Unknown Input Decoupling and Fault Estimation. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-19258-1_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19257-4
Online ISBN: 978-3-319-19258-1
eBook Packages: Computer ScienceComputer Science (R0)