Revista UIS Ingenierías

Vol. 16 Núm. 2 (2017): Revista UIS Ingenierías
Artículos

Estrategias de control estructural basadas en amortiguadores magnetoreológicos administrados utilizando redes neuronales y lógica difusa

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  • Luis Lara,
  • José Brito,
  • Carlos Alberto Graciano Gallego
Luis Lara
University of Brasília, Brazil
José Brito
Universidad Nacional de Colombia
Carlos Alberto Graciano Gallego
Universidad Nacional de Colombia Facultad de Minas- Sede Medellín Departamento de Ingeniería Civil
DOI: https://doi.org/10.18273/revuin.v16n2-2017021

Publicado 2017-05-15

Palabras clave

  • Control de estructuras,
  • reducción de vibraciones,
  • amortiguadores magnetoreológicos,
  • redes neuronales artificiales,
  • lógica difusa

Cómo citar

Lara, L., Brito, J., & Graciano Gallego, C. A. (2017). Estrategias de control estructural basadas en amortiguadores magnetoreológicos administrados utilizando redes neuronales y lógica difusa. Revista UIS Ingenierías, 16(2), 227–242. https://doi.org/10.18273/revuin.v16n2-2017021
Aviso de derechos de autor/a

Resumen

En este trabajo se presenta una evaluación numérica sobre el desempeño de dos estrategias de control estructural basado en amortiguadores magnetoreológicos (MR).  En primer lugar, se empleó una estrategia de control basada en redes neuronales artificiales en una estructura simple para el control de vibraciones.  Este controlador combina una función de modelo predictivo para las fuerzas de control y un modelo inverso del cálculo de la tensión para manejar los amortiguadores MR. En segundo lugar, se utilizó una estrategia de control basada en lógica difusa. De esta forma, el controlador gobierna las acciones de un conjunto de reglas que representan la heurística del sistema a controlar.  Los resultados de las simulaciones numéricas indican que el rendimiento de estas dos estrategias de control es prometedor y satisfactorio, basado en la reducción de la respuesta de hasta un 83% en relación con el rendimiento del sistema sin control.

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Referencias

  1. Y. Kim, R. Langari, S. Hurlebaus. ”Semiactive nonlinear control of a building with magnetorheological damper system”. Mechanical systems and signal processing, volume 23, no. 2, pp. 300-315, Feb. 2009.
  2. G. Yang. “Large-Scale Magnetorheological Fluid Damper for Vibration Mitigation: Modeling, Testing and Control”, PhD Thesis, Department of Civil Engineering and Geological Sciences, University of Notre Dame, Indiana, USA, 2001.
  3. S. Dyke. “Acceleration feedback control strategies for active and semi-active control systems: modeling, algorithm development, and experimental verification”, PhD Thesis, Department of Civil Engineering and Geological Sciences, University of Notre Dame, Indiana, USA, 1996.
  4. C.M. Dias. “Fuzzy Control of Magnetorheological Dampers for Vibration Reduction of Seismically Excited Structures”, Ph.D. Thesis, Florida State University, Florida, USA, 2005.
  5. R. Ehrgott, S. Masri. “Modeling the oscillatory dynamic behavior of electrorheological materials in shear”. Smart materials and structures, vol 1, no. 4, pp.275-285, 1992.
  6. N. McClamroch, H. Gavin. “Electrorheological dampers and semi-active structural control”, 34th conference on decision & control, New Orleans, USA, 1995.
  7. H. Gavin, R. Hanson, F. Filisko. ”Electrorheological dampers part II: testing and modeling”. Journal of applied mechanics, vol. 63, no. 2, pp.676-682, Sep. 1996.
  8. X. Song, M. Ahmadian, S. “Southward. Modeling magnetorheological dampers with application of nonparametric approach”. Journal of intelligent material systems and structures, vol. 16, pp.421-432, May. 2005.
  9. J. Zhang, P. Roschke. “Neural network simulation of magnetorheological damper behavior”, the international conference on vibration engineering, China, 1998.
  10. D. Wang, W. Liao. “Neural network modeling and controllers for magnetorheological fluid dampers”, the international fuzzy systems conference, Melbourne, Australia, 2001.
  11. P. Xia. “An inverse model of MR damper using optimal neural network and system identification”. Journal of sound and vibration, vol. 266, no. 5, pp.1009-1023, Oct. 2003.
  12. D. Wang, W. Liao. “Modeling and control of magnetorheological fluid dampers using neural networks”. Smart materials and structures, vol. 14, no. 1, pp. 111-126, Dec. 2004.
  13. K. Schurter, P. Roschke. “Fuzzy modeling of a magnetorheological damper using ANFIS”, IEEE fuzzy conference, San Antonio, USA, 2000.
  14. M. Giuclea, T. Sireteanu, D. Stancioiu, C. Stammers. “Modelling of Magnetorheological damper dynamic behavior by genetic algorithms based inverse method”, House of the Romanian Academy, vol. 5, no. 1, 2004.
  15. N. Kwok, Q. Ha, M. Nguyen, J. Li, B. Samali. “Bouc-Wen model parameter identification for a MR fluid damper using computationally efficient GA”. ISA Transactions, vol. 46, no. 2, pp.167-179, Apr. 2007.
  16. M. Boada, J. Calvo, B. Boada, V. Diaz. “Modeling of a magnetorheological damper by recursive lazy learning”. International Journal of Non-linear Mechanics, vol. 46, no. 3. pp. 1-7, Apr. 2011.
  17. Stanway, R., Sproston, J., Stevens, N. “Non-linear identification of an electro-rheological vibration damper”, Symposium on identification and system parameter estimation, York, England, 1985.
  18. R. Stanway, J. Sproston, N. Stevens. “Non-linear modeling of an electro-rheological vibration damper”. Journal of Electrostatics, vol. 20, no. 2, pp.167-184, Dec. 1987.
  19. D. Gamota, F. Filisko. “Dynamic mechanical studies of electrorheological materials: Moderate frequencies”. Journal of Rheology, vol 35, no. 3, pp.399-425, 1991.
  20. Jr. Spencer, B.F.; S.J. Dyke, M.K. Sain, J.D. Carlson. “Phenomenological model of a magnetorheological damper”. Journal of Engineering Mechanics, vol. 123, no. 3, pp.230-238, Mar. 1997.
  21. J. Carlson, Jr. Spencer. “Magneto-rheological fluid dampers: scalability and design issues for application to dynamic hazard mitigation”, The 2nd workshop on structural control: Next generation of intelligent structures, China, 1996.
  22. F. Gordaninejad, M. Saiidi, B. Hansen, F. Chang. ”Magnetorheological fluid dampers for control of bridges”. The 2nd World Conference on Structural Control, Chichester, U.K, 1999.
  23. L. Jansen, S. Dyke. “Semiactive control strategies for MR dampers: Comparative study”. Journal of Engineering Mechanics, vol. 126, no. 8, pp.795-803, Aug. 2000.
  24. Y. Liu, F. Gordaninejad, C. Evrensel, G. Hitchcock. “Experimental Study on Fuzzy Logic Vibration Control of a Bridge Using Fail-Safe Magneto-Rheological Fluid Dampers”, Smart Structures and Materials 2001: Smart Systems for Bridges, Structures, and Highways,, vol. 4330, 2001.
  25. M. Basili. “Controllo semi attivo di strutture adiacenti mediante dispositivi magnetoreologici: teoria, sperimentazione e modellazione”, Ph.D. Thesis, Università degli studi di Roma “La Sapienza”, Roma, Italy, 2006.
  26. G. Yan, L. Zhou. “Integrated fuzzy logic and genetic algorithms for multi-objective control of structures using MR dampers”. Journal of Sound and Vibration, vol. 296, no. 1-2, pp. 368-382, Sep. 2006.
  27. P. Lin, P. Roschke, C. Loh. ”Hybrid base-isolation with magnetorheological damper and fuzzy control”. Struct. control health monit, vol. 14, no. 1, pp. 384-405, Mar. 2006.
  28. R.B. Carneiro. “Semi-active control of vibrations in structures using magnetorheological damper”, Ph.D. Thesis, University of Brasilia, Brasilia, Brazil, 2009.
  29. H. Metered, P. Bonello, S. Oyadiji. “The experimental identification of magnetorheological dampers and evaluation of their controllers”. Mechanical Systems and Signal Processing, vol. 24, no. 4, pp. 976-994, May. 2010.
  30. L.A. Lara. “Study of semi-active control algorithms applied to magnetorheological dampers”, Ph.D. Thesis, Department of Civil Engineering, University of Brasília, Brasilia, Brazil, 2011.
  31. S. Haykin. “Redes Recorrentes Dirigidas Dinamicamente”, em Redes neurais princípios e prática. 2nd edição. São Paulo, Brazil: Bookman, cap. 15, pp. 787-843.
  32. X. He, H. Asada. “A new method for identifying orders of input-output models for nonlinear dynamic systems”, American Control Conference, San Francisco, California, USA, pp. 2520-2523, 1993.
  33. K. Levenberg. “A method for the solution of certain non-linear problems in least squares”, Quart. Appl. Math., vol. 2, no. 2, pp. 2164-2168, Jul. 1944.
  34. D.W Marquardt. “An algorithm for least-squares estimation of nonlinear parameters”, Journal of the society for industrial and applied mathematics, vol. 11, no. 2, pp. 431-441, Jun. 1963.
  35. L. Lara, J. Brito, Y. Valencia. “Structural control using magnetorheological dampers governed by predictive and dynamic inverse models”, Dyna, vol 81, no.188, pp.191-198, 2014.
  36. M. Aldawod, B. Samali,, F. Naghdy, K. Kwok. ”Active control of a long wind response of tall building using a fuzzy controller”, Engineering structures, vol. 23, no. 11, pp.1512-1522, Nov. 2001.
  37. F. Marazzi, G. Magonette. “Active and semi-active control of structures: A comparison”, European meeting on intelligent structures, Italia, 2001.
  38. A. Contento, V. Gattulli, M. Lepidi, F. Potenza. Identificazione di modelli per un protótipo sperimentale di telaio tridimensionale com controventi semi-attivi, Università degli studi dell’Aquila, Technical Report no.2, 2006.
  39. Lord technical data RD-1005-3 Damper, Lord Corporation, Technical data, Cary, USA, 2006.