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Nonlinear System Identification of Multi-Degree-of-Freedom Systems

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Abstract

A nonlinear system identification methodology based on theprinciple of harmonic balance is extended tomulti-degree-of-freedom systems. The methodology, called HarmonicBalance Nonlinearity IDentification (HBNID), is then used toidentify two theoretical two-degree-of-freedom models and anexperimental single-degree-of freedom system. The three modelsand experiments deal with self-excited motions of afluid-structure system with a subcritical Hopf bifurcation. Theperformance of HBNID in capturing the stable and unstable limitcycles in the global bifurcation behavior of these systems is alsostudied. It is found that if the model structure is well known,HBNID performs well in capturing the unknown parameters. If themodel structure is not well known, however, HBNID captures thestable limit cycle but not the unstable limit cycle.

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References

  1. Abarbanel, H. D. I., Analysis of Observed Chaotic Data, Springer-Verlag, New York, 1996.

    Google Scholar 

  2. Billings, S. A., 'Identification of nonlinear systems - A survey', Proceedings of IEEE 127, 1980, 272–285.

    Google Scholar 

  3. Casas, R. A., Jacobson, C. A., Rey, G. J., Green, M., and Johnson, C. R., 'Harmonic balance methods for nonlinear feedback identification', Allerton 2, 1997, 230.

    Google Scholar 

  4. Chen, S. and Billings, S. A., 'Representations of nonlinear systems: The NARMAX model', International Journal of Control 49(3), 1989, 1013–1032.

    Google Scholar 

  5. Haber, R. and Unbehauen, H., 'Structure identification of nonlinear dynamic systems - A survey in input/output approaches', Automatica 26, 1990, 651–677.

    Google Scholar 

  6. Hagedorn, P., Non-Linear Oscillations, Oxford University Press, Oxford, 1988.

    Google Scholar 

  7. Ljung, L., System Identification: Theory for the User, Prentice-Hall, Englewood Cliffs, NJ, 1987.

    Google Scholar 

  8. Masri, S. F., Chassiakos, A. G., and Caughey, T. K., 'Identification of nonlinear dynamic systems using neural networks', ASME, Journal of Applied Mechanics 60, 1993, 123–133.

    Google Scholar 

  9. Moon, F. C., Chaotic and Fractal Dynamics, Wiley, New York, 1992.

    Google Scholar 

  10. Thothadri, M., 'Nonlinear system identification and control of fluid-elastic vibrations of a cylinder row using bifurcation theory', Ph.D. Thesis, Cornell University, 1999.

  11. Thothadri, M. and Moon, F. C., 'Helical wave oscillations in a row of cylinders in a cross flow', Journal of Fluids and Structures 12, 1998, 591–613.

    Google Scholar 

  12. Thothadri, M. and Moon, F. C., 'An investigation of nonlinear models for a cylinder row in a cross flow', ASME, Journal of Pressure Vessel Technology 121, 1999, 133–141.

    Google Scholar 

  13. Yasuda, K., Kawamura, S., and Watanabe, K., 'Identification of nonlinear multi-degree-of-freedom systems', JSME International Journal 31(1), 1988, 8–15.

    Google Scholar 

  14. Yuan, C. M. and Feeny, B. F., 'Parametric identification of chaotic systems', Journal of Vibration and Control 4, 1998, 405–426.

    Google Scholar 

Download references

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Thothadri, M., Casas, R.A., Moon, F.C. et al. Nonlinear System Identification of Multi-Degree-of-Freedom Systems. Nonlinear Dynamics 32, 307–322 (2003). https://doi.org/10.1023/A:1024489210804

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