Design of Fault Tolerant System Using Model Predictive Control and Model-Based Fault Identification for a Chemical Reactor
Subject Areas : Renewable energy
Mehrdada Raeiisi
1
(
Department of Electrical Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran
)
Seyed Mohammad Kargar Dehnavi
2
(
Smart Microgrid Research Center- Najafabad Branch, Islamic Azad University, Najafabad, Iran
)
Keywords: Model predictive control, Unscented Kalman Filter, Chemical Continuously Stirred Tank Reactor, Fault-Tolerant control,
Abstract :
Due to the possibility of fault in any industrial system's actuators, using a fault-tolerant control structure to compensate for the fault and maintain the system stability seems necessary. In this paper, the Continuously Stirred Tank Reactor model is evaluated, which has a nonlinear model with temperature outputs and heating inlets of interconnected tanks. An Unscented Kalman filter is used to estimate the model's output dynamics, which has a suitable convergence speed and higher accuracy than other estimators. The nonlinear predictive control approach is used to apply the appropriate heating rate to the system to achieve the desired temperatures for each tank when there is no fault in the system. In the proposed design, to compensate for the fault, a sliding mode observer has been used to identify the fault. When a fault is detected, a fuzzy proportional derivative controller is used to control the system's fault. MATLAB software has been to evaluate the proposed method in different working modes of the reactor model. The simulation results show the good performance of the proposed method to compensate for the fault
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_||_[1] R. Lakhani, C.R. Srinivasan, S. Ra, "Transient analysis of systems exhibiting inverse response and their control with CSTR as a case study", Indonesian Journal of Electrical Engineering and Computer Science, vol. 20, no. 1, pp. 89–99, 2020 (doi:10.11591/ijeecs.v20.i1.pp89-99).
[2] J. Lan and R.J. Patton, "A new strategy for integration of fault estimation within fault-tolerant control", Automatica, vol. 69, pp. 48–59, 2016 (doi:10.1016/j.automatica.2016.02.014).
[3] B. Kuipers, K. Astrom, "The composition and validation of heterogeneous control laws", Automatica, vol. 30, no. 2, pp.233–49, 1994 (doi:10.1016/0005-1098(94)90027-2).
[4] B. Aufderheide, B. W. Bequette, "Extension of dynamic matrix control to multiple models", Computers and Chemical Engineering, vol. 27, no. 8–9, pp. 1079–1096, 2003 (doi:10.1016/S0098-1354(03)00038-3).
[5] Z. Tian, K.A. Hoo, "Multiple model-based control of the Tennessee-Eastman process", Industrial and Engineering Chemistry Research, vol. 44, no. 9, pp. 3187–3202, April 2005 (doi:10.1021/ie0496939).
[6] P. Lu, E.J.V. Kampen, C. de Visser, Q. Chu, "Aircraft fault-tolerant trajectory control using incremental nonlinear dynamic inversion", Control Engineering Practice, vol. 57, pp. 126–141, Dec. 2016 (doi: 10.1016/j.conengprac.2016.09.010).
[7] A.J. Gonzalez, G. Nencioni, B.E. Helvik, A. Kamisinski, "A fault-tolerant and consistent sdn controller", Proceeding of the IEEE/GLOBECOM), pp. 1-6, Washington, DC, USA, Dec. 2016 (doi: 10.1109/glocom.2016.7841496).
[8] E.P. Nahas, M.A. Henson, D.E. Seborg, "Nonlinear internal model control strategy for neural network models", Computers and Chemical Engineering, vol. 16, no. 12, pp. 1039–1057, Dec. 1992 (doi:10.1016/0098-1354(92)80022-2).
[9] J.M.R. Chintu, R.K. Sahu, "Differential evolution optimized fuzzy PID controller for automatic generation control of interconnected power system", Advances in Intelligent Systems and Computing, vol. 1120, pp. 123–132, Feb. 2020 (doi:10.1016/j.jprocont.2020.08.006).
[10] C.T. Chao, N. Sutarna, J.S. Chiou, C.J. Wang, "An optimal fuzzy PID controller design based on conventional PID control and nonlinear factors", Applied Sciences, vol. 9, no. 6, Article Number: 1224, 2019 (doi: 10.3390/app9061224).
[11] R. Mehrad , S.M. Kargar. "Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit", Transactions of the Institute of Measurement and Control, vol. 42, no. 10, pp. 1882-1894, Jan. 2020 (doi:10.1177/0142331219898942).
[12] X. Yang, J.M. Maciejowski, "Fault-tolerant model predictive control of a wind turbine benchmark", IFAC Proceedings Volumes, vol. 45, no. 20, pp. 337–342, Jan. 2012(doi:10.3182/20120829-3-MX-2028.00134).
[13] R. Senthil, K. Janarthanan, J. Prakash, "Nonlinear state estimation using fuzzy Kalman filter", Industrial and Engineering Chemistry, vol. 45, no. 25, pp. 8678–8688, Dec. 2006 (doi:10.1021/ie0601753).
[14] H. Mekki, O. Benzineb, D. Boukhetala, M. Tadjine, M. Benbouzid, "Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems", ISA Transactions., vol. 57, pp. 340–351, July 2015 (doi: 10.1016/j.isatra.2015.02.004).
[15] H. Alwi, C. Edwards, C.P. Tan, "Fault detection and fault-tolerant control using sliding modes", Springer, London, 2011 (doi:10.1007/978-0-85729-650-4).
[16] H. Yang, M. Saif, "Fault detection in a class of nonlinear systems via adaptive sliding mode observer", Proceeding of the IEEE/ICSMC, vol. 3, pp. 2199-2204, Vancouver, BC, Canada, Oct. 1995 (doi: 10.1109/ICSMC.1995.538107).
[17] J. Lan, R.J. Patton, "Integrated design of robust fault estimation and fault-tolerant control for linear systems", Proceedings of the IEEE/CDC, pp. 5105–5110, 2015 (doi:10.1109/CDC.2015.7403018).
[18] P. Mhaskar, J. Liu, P.D. Christofides, "Fault-tolerant process control", Methods and applications,
Springer, 2013 (ISBN: 978-1-4471-4808-1).
[19] Q. Shen, C. Yue, C. H. Goh, D. Wang, "Active fault-tolerant control system design for spacecraft attitude maneuvers with actuator saturation and faults", IEEE Trans. on Industrial Electronics, vol. 66, no. 5, pp. 3763–3772, May 2019 (doi:10.1109/TIE.2018.2854602).
[20] M. Khalili, X. Zhang, M. M. Polycarpou, T. Parisini, Y. Cao, "Distributed adaptive fault-tolerant control of uncertain multi-agent systems", Automatica, vol. 87, pp. 142–151, Jan. 2018 (doi: 10.1016/j.automatica.2017.09.002).
[21] S. DIng, W.H. Chen, K. Mei, D.J. Murray-Smith, "Disturbance observer design for nonlinear systems represented by input-output models", IEEE Trans. on Industrial Electronics, vol. 67, no. 2, pp. 1222–1232, Feb. 2020 (doi:10.1109/TIE.2019.2898585).
[22] W. Qi, G. Zong, H.R. Karimi, "Finite-time observer-based sliding mode control for quantized semi-markov switching systems with application", IEEE Trans. on Industrial Informatics, vol. 16, no. 2, pp. 1259–1271, Feb. 2020 (doi: 10.1109/TII.2019.2946291).
[23] W. Chen, F.N. Chowdhury, "A synthesized design of sliding-mode and Luenberger observers for early detection of incipient", International Journal of Adaptive Control and Signal Processing, vol. 24, no. 12, pp. 1021–1035, Dec. 2010 (doi:10.1002/acs.1170).
[24] L. Fridman, A. Levant, J. Davila, "Observation of linear systems with unknown inputs via high-order sliding-modes", International Journal of Systems Science, vol. 38, no. 10, pp. 773–791, Jan. 2007 (doi: 10.1080/00207720701409538).
[25] S.M. Kargar, K. Salahshoor, M.J. Yazdanpanah, "Multiple model-based fault detection and diagnosis for nonlinear model predictive fault-tolerant control", International Journal of Systems Science, vol. 39, no. 10, pp. 7433–7442, Sept. 2014 (doi:10.1007/s13369-014-1252-y).
[26] E.A. Wan, R.V.D. Merwe, "The unscented Kalman filter for nonlinear estimation", Proceeding of the IEEE/AS-SPCC, pp. 153–158, Lake Louise, AB, Canada, Oct. 2000 (doi: 10.1109/ASSPCC.2000.882463).
