Damping of Structural Vibrations Using Adaptive Joint Connections and Neural Control

  • Hans Albrecht
  • Jan Wirnitzer
  • Lothar Gaul
Part of the International Centre for Mechanical Sciences book series (CISM, volume 429)


In the present paper, a concept for vibration suppression of flexible structures based on controlled energy dissipation in adaptive joint connections is presented. First, the damping effect of dry friction on the dynamical behaviour of a two-dimensional truss structure consisting of five struts and one bolted joint connection is investigated experimentally and numerically. Experiments are carried out with varied normal force in the bolted joint in order to analyse the influence on the vibration characteristics of the flexible structure. For the experimental structure a FE formulation is presented including a model of the frictional joint connection. Finally, a neural control approach for the adaptive joint connections is described. An application example illustrates this so-called semi-active vibration damping concept.

The present paper shows that this concept significantly enhances the vibration suppression of a flexible lightweight structure. Based on these results. an adaptive three-dimensional large space structure with adaptive joints is in reach in the near future.


Normal Force Truss Structure Vibration Suppression Neural Controller Active Vibration Control 


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  1. Bathe, K.-J. (1982). Finite Elemente Procedures in Engineering Analysis. Englewood Cliffs/NJ: Prentice Hall.Google Scholar
  2. Beards, C. F., and Woowat, A. (1985). The control of frame vibration by friction damping in joints. Journal of Vibration, Acoustics, Stress, and Reliability in Design 107: 26–32.CrossRefGoogle Scholar
  3. Breitbach, E. J., Lammering, R., Melcher, J., and Nitzsche, F. (1994). Smart structures research in aerospace engineering. In European Conference on Smart Structures and Materials, 11–18.Google Scholar
  4. Canudas de Wit, C., Olsson, H., Aström, K. J., and Lischinsky, P. (1995). A new model for control of systems with friction. IEEE Transactions on Automatic Control 40 (3): 419–425.CrossRefMATHGoogle Scholar
  5. Ewins, D. J., and Vakakis, A. F. (1992). Effects of weak nonlincarities on modal analysis. ln Proceedings of the Intern. Modal Analysis Conference (MAC), 72–78.Google Scholar
  6. Ferri, A. A., Heck, B. S., and Lane, J. S. (1992). Vibration control using semi-active friction damping. In R. Ibrahim, A. S., ed.. Friction-Induced -ïhration, Chatter Squeal. and Chaos. ASME-Publication, volume DE-Vol. 49. 165–171.Google Scholar
  7. Folkman, S., Ferney. B., Bingham, J., and Dutson, J. (1996). Friction and impact damping in a truss using pinned joints. In Guran, A., Pfeiffer. F. and K. Popp, eds., Dynamics with Friction: Modeling. Analysis and Experiment. Series on Stability, Vibration und Control of Systems, Series B, volume 7. World Scientific Publishing Co. 137–168.CrossRefGoogle Scholar
  8. Gaul, L. and Lenz, J. (1997). Nonlinear dynamics of structures assembled by bolted joints. Acta Meehanica 125: 169–181.CrossRefMATHGoogle Scholar
  9. Gaul, L., and Nitsche, R. (1999). Nonlinear dynamics of structures with joint connections. In D. J. Ewins. D. J. I., ed., Structural Dynamics 2000, Series Engineering Dynamics, volume DE-Vol. 49. 165–171. to appear.Google Scholar
  10. Gaul, L., Lenz. J., and Sachau, D. (1998). Active damping of space stnuctures by contact pressure control in joints. Mechanics of Structures and Machines 26 (31): 81–100.CrossRefGoogle Scholar
  11. Hrycej, T. (1994). Model-based training of control sequences in neural controllers. Neural Network World 2: 173–188.Google Scholar
  12. Hunt, K. J., Sbarbaro, D., Zbikowski, R., and Gawthrop, P. J. (1992). Neural networks for control systems — a survey. Autour atica 28(6): 1083–1 112.Google Scholar
  13. Nitsche, R., and Gaul, L. (September 12–15. 1999 ). Friction control for vibration suppression. in Prceedings of the ASME Design Engineering Technical Conference, volume DECT99/ViB-8191.Google Scholar
  14. Preumont, A., and Bossons, F. (1999). Active tendon control of a truss structure. In International Conference on Adaptive Structures and Technologies. 305–312. Paris: Technomic Publishing.Google Scholar
  15. Valanis, K. C. (1991). Fundamental consequences of a new intrinsic time measure. plasticity as a limit of the endochronic theory. Archive of Mechanics 32: 171–191.Google Scholar
  16. Werbos, P. J. (1990). A menu of designs for reinforcement learning over time. In Miller. T. W., Sutton, R. S., and Werbos, P. J., Eds., Neural Networks fin Control. MIT Press. chapter 1, 67–96.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Hans Albrecht
    • 1
  • Jan Wirnitzer
    • 1
  • Lothar Gaul
    • 1
  1. 1.Institute A of MechanicsUniversity of StuttgartStuttgartGermany

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