A New Fault Tolerant Nonlinear Model Predictive Controller Incorporating an UKF-Based Centralized Measurement Fusion Scheme

Document Type : Research Note

Authors

Department of Automation and Instrumentation, Petroleum University of Technology, Tehran, I.R. IRAN

Abstract

A new Fault Tolerant Controller (FTC) has been presented in this research by integrating a Fault Detection and Diagnosis (FDD) mechanism in a nonlinear model predictive controller framework. The proposed FDD utilizes a Multi-Sensor Data Fusion (MSDF) methodology to enhance its reliability and estimation accuracy. An augmented state-vector model is developed to incorporate the occurred sensor faults and then a UKF algorithm is utilized to estimate the augmented state vector including system states along with the fault terms using a centralized measurement fusion scheme. The designed FDD architecture is then merged with a conventional NMPC to form a Fault-Tolerant Control System (FTCS). A series of sensor fault senarios is conducted on a Continuous Stirred Tank Reactor (CSTR) to comparatively illustrate the superiority of the proposed FTCS in eliminating the miserable impacts of the induced sensor faults against a conventional NMPC.

Keywords

References

[1] Visscher, R., ChernobylNuclear Disaster. http://www.chernobul.co.ul/hoef.html ,(1990).
[2] Maciejowski J. M., Modeling and Predictive Control: Enabling Technologies for Reconfiguration, Annual Reviews in Control, 23, p. 13 (1999).
[3] Pranatyasto T.N., Qin S.J., Sensor Validation and Process Fault Diagnosis for FCC Units under MPC Feedback, Control Engineering Practice, 9, p. 877 (2001).
[4] Prakash J., Patwardhan S.C., Narasimhan S.A., Integrating Model Based Fault Diagnosis with Model Predictive Control, Ind. Eng. Chem. Res., 44, p. 4344 (2005).
[5] Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N., A Review of Process Fault Detection and Diagnosis Part I: Quantitative Model-Based Methods, Computers and Chemical Engineering, 27, p. 293 (2003).
[6] Julier S.J., Uhlmann J.K., Durrant-Whyte H., A New Approach for Filtering Non-Linear Systems, in "Proceedings of the American Control Conference", p. 1628 (1995).
[7] Julier S.J., Uhlmann J.K., A New Extension of the Kalman Filter to Non-linear Systems, in "Proc. Of AeroSense: The 11th Int. Symp. A.D.S.S.C", (1997).
[8] Grewal M., Andrews A.P., “Kalman Filtering, Theory and Practice Using Matlab”, Third Edition, John Wiley & Sons, (2008).
[9] Harris C., Bailey A., Dodd T., Multisensor Data Fusion in Defense and Aerospace, Journal of Royal Aerospace Society, 162, p. 229 (1998).
[10] Sørensen H., Nørgaard M., Ravn O., Oulsen N.K., Implementation of Neural Network Based Non-Linear Predictive Control, Neurocomputing, 28, p. 37 (1999).
[11] Dennis J.E, Schnabel R.B., “Numerical Methods for Unconstrained Optimization and Nonlinear Equations”, Prentice-Hall, (1997).
[12] Luyben W.L., “Process Modeling Simulation and Control for Chemical Engineers”, 2nd Ed., McGraw-Hill, (1989).
Iranian Journal of Chemistry and Chemical Engineering
Volume 30, Issue 4 - Serial Number 60
December 2011
Pages 107-112

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