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Application of evolving fuzzy modeling to fault tolerant control

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Abstract

This paper proposes the application of evolving fuzzy modeling to fault-tolerant in two steps: fault detection and fault accommodation. Fault accommodation uses evolving Takagi–Sugeno fuzzy models, and fault detection uses a model-based approach also based on fuzzy models. Information from fault detection is used for fault accommodation in a model predictive control (MPC) scheme. The evolving fuzzy modeling approach increases the control performance when the process is with faults. The proposed approach continuously evaluate the control performance and perform on-line clustering, when necessary. Evolving FTC is used to accommodate two simulated faults in a distillation column process. The considered faults are the load process fault (variation in feed composition) and the change in heating (variation of re-boiler temperature). The fault tolerant control using evolving fuzzy modeling was able to accommodate the simulated faults.

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References

  • Angelov P, Buswell R (2002) Identification of evolving fuzzy rule-based models. IEEE Trans Fuzzy Syst 667–678

  • Angelov P, Filev D (2005) Simpl eTS: A Simplified Methd for Learning Evolving Takagi–Sugeno Fuzzy Models. In: Proceedings IEEE international conference on fuzzy systems, pp 1068–1073

  • Benkhedda H, Patton R (1997) Information Fuzzion in Fault Diagnosis Based on B-spline Networks. SAFEPROCESS’97, Preprints of the IFAC Symposium on fault detection, supervision and safety for technical processes, vol 2. Kingstom Upon, Hull, pp 681–686

  • Blanke M, Kinnaert M, Lunze J, Staroswiecki M (2003) Diagnosis and fault-tolerant control. Springer, Berlin

    MATH  Google Scholar 

  • Calado JMF, Korbicz J, Patan K, Patton RJ, Sá da Costa JMG (2001) Soft computing approaches to fault diagnosis for dynamic systems. Eur J Control 7(2–3):169–208

    Google Scholar 

  • Camacho EF, Bordons C (1995) Model predictive control in the process industry. Springer, London

    Google Scholar 

  • Chen R, Patton R (1999) Robust model-based fault diagnosis for dynamic systems. Kluwer, Boston

    MATH  Google Scholar 

  • Deibert R (1994) Model Based Fault Detection of Valves in Flow Control Loops SAFEPROCESS’94, Preprints of the IFAC Symposium on fault detection, supervision and safety for technical processes, Espoo, Finland, pp 445–450

  • Gopinathan M, Boskovic JD, Mehra RK, Rago C (1998) A multiple model predictive scheme for fault-tolerant flight control design. In: Proc. Conf. Decision and Control. Tampa

  • Ichtev A, Hellendoorn J, Babuška R, Mollov S (2002) Fault-tolerant model-based predictive control using multiple Takagi–Sugeno fuzzy models. In: Proceedings of the IEEE international conference on fuzzy systems, FUZZ-IEEE’02 1, vol 12–17. pp 346–351

  • Ingimundarson A, Sanchez-Pena R (2008) Using the unfalsified control concept to achieve fault tolerance. In: Proceedings of the 17th IFAC Word Congress. Seoul, Korea, pp 1236–1242

  • Koscielny JM (1999) Application of fuzzy logic fault isolation in a three-tank system. In: 14th world congress IFAC, vol P-7e-05-1. Beijing, pp 73–78

  • Koscielny JM, Syfert M (2003) Fuzzy logic applications to diagnostics of industrial processes. Preprints of the 5th IFAC symposium on fault detection, supervision and safety for technical processes, SAFEPROCESS’2003. Washington, June, pp 771–776

  • Lee PL, Bao J (2007) Process control. Springer, Berlin

  • Lopez-Toribio CJ, Patton RJ, Daley S (2000) Takagi–Sugeno fuzzy fault-tolerant control of an induction motor. Neural Comput Appl 9:19–28

    Article  Google Scholar 

  • Maciejowski JM, Jones CN (2003) MPC fault-tolerant flight control case study: flight 1862 IFAC symposium SAFEPROCESS’2003. Washington, pp 121–125

  • Maciejowski JM (2002) Predictive control with constrains. Prentice-Hall, New Jersey

  • Massoumia MA, Van der Velde WE (1988) Generating parity relation for detecting and identifying system component failures. J Guid 11:60–65

    Article  Google Scholar 

  • Mediavilla M, de Miguel LJ, Vega P (1997) Isolation of Multiplicative Faults in the Industrial Actuator Benchmark SAFEPROCESS’97, Preprints of the IFAC Symposium on fault detection, supervision and safety for technical processes, vol. 11. Kingstom Upon, Hull, pp 855–860

  • Mendonça LF (2007) Controlo tolerante a falhas baseados em modelos fuzzy, Ph.D. Dissertation, Instituto Superior Técnico, Dez

  • Mendonça LF, Sousa JMC, Sá da Costa JMG (2009) An architecture for fault detection and isolation based on Fuzzy methods. Expert Syst Appl Int J 36(2):1092–1134

    Article  Google Scholar 

  • Mendonça LF, Sousa JMC, da Costa JMGS (2004) Optimization problems in multivariable fuzzy predictive control. Int Journal Approx Reason 36(3):199–221

    Article  MATH  Google Scholar 

  • Niemann H, Stoustrup J (2005) Passive fault tolerant control of a double inverted pendulum: a case study. Control Eng Pract 13:1047–1059

    Article  Google Scholar 

  • Niemann H, Stoustrup J (2002) Reliable control using the primary and dual Youla parameterization. In: Proceedings of the 41st IEEE Conference on Decision and Control. Las Vegas, pp 4353–4358

  • Oehler R, Schoenhoff A, Schreiber M (1997) On-line Model Based Fault Detection and Diagnosis for a Smart Aircreft Actuator SAFEPROCESS’97, Preprints of the IFAC Symposium on fault detection, supervision and safety for technical processes, vol. 11. Kingstom Upon, Hull, pp 591–596

  • Oliveira P, Batalha N, Sousa T, Supervised by Prof. C. Pinheiro, Silva J (2008)“Relatorio Técnico - Coluna de Destilação Piloto. Set-up Experimental e Modelo”, POCI/EME/59522/04, Junho de

  • Patton RJ (1997) Fault-tolerant control systems: The 1997 situation. In: Proceedings of the 3rd IFAC Symposium. Kingston Upon Hull, UK, pp 26–28

  • Phatak M, Wiswanadham N (1988) Actuator fault detection and isolation in linear systems. Int J Sys Sci 19(12):2593–2603

    MATH  Google Scholar 

  • Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. Control Eng Pract 11(12):733–764

    Article  Google Scholar 

  • Reznik L (1997) Fuzzy controllers. Newnes Press

  • Sousa JMC, Kaymak U (2002) Fuzzy decision making in modeling and control. World Scientific

  • Staroswiecki M (2005) Fault tolerant control using an admissible model matching approach. In: Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain, pp 2421–2426

  • Staroswiecki M, Yang H, Jiang B (2006) Progressive accommodation of aircraft actuator faults. In: Proceedings of the 6th IFAC Symposium of Fault Detection Supervision and Safety for Technical Processes, August–September, Beijing, pp 877–882

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modelling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    MATH  Google Scholar 

  • Tay TT, Mareels IMY, Moore JB (1997) High performance control. Basel, Birkhauser

  • Wang W, Vrbanek J (2008) An evolving fuzzy predictor for industrial applications. IEEE Trans Fuzzy Syst 16(6):1439–1449

    Article  Google Scholar 

  • Zhang YM, Jiang J (2003) Bibliographical Review on Reconfigurable Fault-Tolerant Control Systems. In: Proceedings of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS’03). Washington, DC, pp 265–276

  • Zhang YM, Jiang J (2006) Issues on integration of fault diagnosis and reconfigurable control in active fault-tolerant control systems. In: Proceedings of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS’06). Beijing, pp 1513–1524

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Acknowledgments

This work was funded by FTC under the scope of the Associated Laboratory in Energy, Transports, Aeronautics and Space.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Instituto Superior Técnico, Technical University of Lisbon, CIS/IDMEC, LAETA, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    D. Chivala, L. F. Mendonça, J. M. C. Sousa & J. M. G. Sá da Costa

  2. Department of Marine Engineering, Escola Superior Náutica Infante D. Henrique, Av. Eng. Bonneville Franco, 2770-058, Lisbon, Portugal

    L. F. Mendonça

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  1. D. Chivala
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  2. L. F. Mendonça
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  3. J. M. C. Sousa
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  4. J. M. G. Sá da Costa
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Correspondence to J. M. C. Sousa.

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Chivala, D., Mendonça, L.F., Sousa, J.M.C. et al. Application of evolving fuzzy modeling to fault tolerant control. Evolving Systems 1, 209–223 (2010). https://doi.org/10.1007/s12530-010-9019-5

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  • DOI: https://doi.org/10.1007/s12530-010-9019-5

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