Smart Structures and Systems

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Structural damage detection using decentralized controller design method

  • Chen, Bilei (Department of Civil and Environmental Engineering, Rice University) ;
  • Nagarajaiah, Satish (Department of Civil and Environmental Engineering, and Department of Mechanical Engineering and Material Sciences, Rice University)
  • Received : 2007.06.20
  • Accepted : 2007.12.21
  • Published : 2008.11.25
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Abstract

Observer-based fault detection and isolation (FDI) filter design method is a model-based method. By carefully choosing the observer gain, the residual outputs can be projected onto different independent subspaces. Each subspace corresponds to the monitored structural element so that the projected residual will be nonzero when the associated structural element is damaged and zero when there is no damage. The key point of detection filter design is how to find an appropriate observer gain. This problem can be interpreted in a geometric framework and is found to be equivalent to the problem of finding a decentralized static output feedback gain. But, it is still a challenging task to find the decentralized controller by either analytical or numerical methods because its solution set is, generally, non-convex. In this paper, the concept of detection filter and iterative LMI technique for decentralized controller design are combined to develop an algorithm to compute the observer gain. It can be used to monitor structural element state: healthy or damaged. The simulation results show that the developed method can successfully identify structural damages.

Keywords

References

  1. Beard, R. V. (1971), "Failure accommodation in linear systems through self-reorganization", Ph.D. Dissertation, Department of Aeronautics and Astronautics, Mass. Inst. Technol., Cambridge, MA.
  2. Cao, Y., Sun, Y. and Mao, W. (1998), "Output feedback decentralized stabilization: ILMI approach", Systems Control Letters, 35, 183-194. https://doi.org/10.1016/S0167-6911(98)00050-4
  3. Dharap, P., Koh, B. H. and Nagarajaiah, S. (2006), "Structural health monitoring using armarkov observers", J. Intell. Mater. Sys. Struct., 17(6), 469-481. https://doi.org/10.1177/1045389X06058793
  4. Douglas, R. K. (1993), "Robust fault detection filter design", Ph.D. dissertation, The University of Texas at Austin.
  5. Douglas, R. K. and Speyer J. L. (1996), "Robust fault detection filter design", J. Guidance, Control. Dyn., 19(1), 214-218. https://doi.org/10.2514/3.21600
  6. Douglas, R. K. and Speyer J. L. (1999), "$H_{\infty}$ bounded fault detection filter", J. Guidance, Control. Dyn., 22(1), 129-138. https://doi.org/10.2514/2.4359
  7. Doebling, S. W., Farrar, C. R., Prime, M. B. and Shevitz, D. W. (1996), "Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review", Research Rep. No. LA-13070-MS, ESA-EA, Los Alamos National Laboratory, N.M.
  8. Frank, P. M. (1990), "Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy − A survey and some new results", Automatica, 26(3), 459-474. https://doi.org/10.1016/0005-1098(90)90018-D
  9. Gao, Y. and Spencer, B. F. (2007), "Experimental verification of a distributed computing strategy for structural health monitoring", Smart Struct. Syst., 4, October.
  10. Ghaoui, L. E., Oustry, F. and AitRami, M. (1997), "A cone complememtarity linearization algorithm for static output-feedback and related problems", IEEE Transactions on Automatic Control, 42(8), 1171-1176. https://doi.org/10.1109/9.618250
  11. Glaser, S. D., Li, H., Wang, M. L., Ou, J. P. and Lynch, J. (2007), "Sensor technology innovation for the advancement of structural health monitoring: a strategic program of US-China research for the next decade", Smart Struct. Sys., 3(2), April.
  12. Jones, H. L. (1973), "Failure detection in linear system", Ph.D. Dissertation, Department of Aeronautics and Astronautics, Mass. Inst. Technol., Cambridge, MA.
  13. Kim, J. T., Park, J. H., Yoon, H. S. and Yi, J. H. (2007), "Vibration-based damage detection in beams using genetic algorithm", Smart Struct. Sys., 3(3).
  14. Koh, B. H., Dharap, P., Nagarajaiah, S. and Phan, M. Q. (2005a), "Real-time structural damage monitoring by input error function", AIAA Journal, 43(8), 1808-1814. https://doi.org/10.2514/1.14008
  15. Koh, B. H., Li, Z., Dharap, P., Nagarajaiah, S. and Phan, M. Q. (2005b), "Actuator failure detection through interaction matrix formulation", J. Guidance, Control, Dyn., 28(5), 895-901. https://doi.org/10.2514/1.11932
  16. Liberatore, S., (2002), "Analytical redundancy, fault detection and health monitoring for structures", Ph.D. Thesis, University of California, Los Angeles, CA.
  17. Massoumnia, Mohammad-Ali. (1986), "A geometric approach to the synthesis of failure detection filters", IEEE Transactions on Automatic Control, 31(9), 839-846. https://doi.org/10.1109/TAC.1986.1104419
  18. Scorletti, G. and Duc, G. (2001), "An LMI approach to decentralized $H_{\infty}$ control", Int. J. Control, 74(3), 211-224. https://doi.org/10.1080/00207170010001966
  19. Sohn, H., Farrar, C. R., Hemez, F. M., Shunk, D. D., Stinemates, D. W. and Nadler, B. R. (2003), A review of structural health monitoring literature: 1996-2001, Los Alamos National Laboratory Report, LA-13976-MS.
  20. Shin, S. and Oh, S. H. (2007), "Damage assessment of shear buildings by synchronous estimation of stiffness and damping using measured acceleration", Smart Struct. Sys., 3(3), July.
  21. White, J. E. and Speyer, J. L. (1987), "Detection filter design: spectral theory and algorithms", IEEE Transactions on Automatic Control, 32(7), 593-603. https://doi.org/10.1109/TAC.1987.1104682
  22. Zhai, G., Ikeda, M. and Fujisaki, Y. (2001), "Decentralized $H_{\infty}$ controller design: a matrix inequality approach using a homotopy method", Automatica, 37, 565-572. https://doi.org/10.1016/S0005-1098(00)00190-4

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