Abstract
The paper is devoted to the problem of a neural network-based robust simultaneous actuator and sensor faults estimator design for the purpose of the Fault Diagnosis (FD) of non-linear systems. In particular, the methodology of designing a neural network-based \(\mathcal {H_\infty }\) fault estimator is developed. The main novelty of the approach is associated with possibly simultaneous sensor and actuator faults. For this purpose, a Linear Parameter Varying (LPV) description of a Recurrent Neural Network (RNN) is exploited. The proposed approach guaranties a predefined disturbance attenuation level and convergence of the estimator. The final part of the paper presents an illustrative example concerning the application of the proposed approach to the multi-tank system fault diagnosis.
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References
Abbas, H., Werner, H.: Polytopic quasi-LPV models based on neural state-space models and application to air charge control of a SI engine. In: Proceedings of the 17th World Congress the International Federation of Automatic Control, Seoul, Korea, pp. 6466–6471 (2008)
Bendtsen, J.D., Trangbæk, K.: Robust quasi-LPV control based on neural state space models. IEEE Trans. Neural Netw. 13(2), 355–368 (2002)
Blesa, J., Rotondo, D., Puig, V., Nejjari, F.: FDI and FTC of wind turbines using the interval observer approach and virtual actuators/sensors. Control Eng. Pract. 24, 138–155 (2014)
Chen, L., Patton, R., Goupil, P.: Robust fault estimation using an LPV reference model: ADDSAFE benchmark case study. Control Eng. Pract. 49, 194–203 (2015)
de Oliveira, M.C., Bernussou, J., Geromel, J.C.: A new discrete-time robust stability condition. Syst. Control Lett. 37(4), 261–265 (1999)
Ducard, G.: Fault-tolerant Flight Control and Guidance Systems: Practical Methods for Small Unmanned Aerial Vehicles. Springer, Berlin (2009)
Gao, Z.-F., Lin, J.-X., Cao, T.: Robust fault tolerant tracking control design for a linearized hypersonic vehicle with sensor fault. Int. J. Control Autom. Syst. 13(3), 672–679 (2015)
Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, New York (2009)
INTECO: Multitank System - User’s manual (2013). www.inteco.com.pl
Isermann, R.: Fault Diagnosis Applications: Model Based Condition Monitoring, Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems. Springer, Berlin (2011)
Li, H., Fu, M.: A linear matrix inequality approach to robust \({H}_\infty \) filtering. IEEE Trans. Sig. Process. 45(9), 2338–2350 (1997)
Li, L., Ding, S.X., Yang, Y., Zhang, Y.: Robust fuzzy observer-based fault detection for nonlinear systems with disturbances. Neurocomputing 174, Part B, 767–772 (2016)
Mahmoud, M., Jiang, J., Zhang, Y.: Active Fault Tolerant Control Systems: Stochastic Analysis and Synthesis. Springer, Berlin (2003)
Mrugalski, M.: Advanced Neural Network-based Computational Schemes for Robust Fault Diagnosis. Springer, Heidelberg (2014)
Mrugalski, M., Luzar, M., Pazera, M., Witczak, M., Aubrun, C.: Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system. ISA Trans. 61, 318–328 (2016)
Noura, H., Theilliol, D., Ponsart, J.C., Chamseddine, A.: Fault-tolerant Control Systems: Design and Practical Applications. Springer, London (2009)
Péni, T., Vanek, B., Szabó, Z., Bokor, J.: Supervisory fault tolerant control of the GTM UAV using LPV methods. Int. J. Appl. Math. Comput. Sci. 25(1), 117–131 (2015)
Rotondo, D., Nejjari, F., Puig, V.: Robust quasi-LPV model reference FTC of a quadrotor uav subject to actuator faults. Int. J. Appl. Math. Comput. Sci. 25(1), 7–22 (2015)
Seybold, L., Witczak, M., Majdzik, P., Stetter, R.: Towards robust predictive fault-tolerant control for a battery assembly system. Int. J. Appl. Math. Comput. Sci. 25(4), 849–862 (2015)
Tayarani-Bathaie, S.S., Vanini, Z.N.S., Khorasani, K.: Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing 125, 153–165 (2014)
Witczak, M.: Toward the training of feed-forward neural networks with the \(\text{ D }\)-optimum input sequence. IEEE Trans. Neural Netw. 17(2), 357–373 (2006)
Witczak, M.: Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems: Analytical and Soft Computing approaches. Springer International Publishing, Heidelberg, Germany (2014)
Yang, H., Wang, H.: Robust adaptive fault-tolerant control for uncertain nonlinear system with unmodeled dynamics based on fuzzy approximation. Neurocomputing 173, Part 3, 1660–1670 (2016)
Yao, L., Feng, L.: Fault diagnosis and fault tolerant tracking control for the non-Gaussian singular time-delayed stochastic distribution system with PDF approximation error. Neurocomputing 175, Part A, 538–543 (2016)
Zemouche, A., Boutayeb, M., Bara, G.I.: Observers for a class of lipschitz systems with extension to \({H}_\infty \) performance analysis. Syst. Control Lett. 57(1), 18–27 (2008)
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The work was supported by the National Science Centre of Poland under grant: 2013/11/B/ST7/01110.
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Pazera, M., Witczak, M., Mrugalski, M. (2017). Neural Network-Based Simultaneous Estimation of Actuator and Sensor Faults. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_27
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DOI: https://doi.org/10.1007/978-3-319-59153-7_27
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