Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-26T13:03:21.063Z Has data issue: false hasContentIssue false

Non-Lipschitzian Control Algorithm for Nanoscale

Published online by Cambridge University Press:  01 February 2011

Friction V. Protopopescu
Affiliation:
Center for Engineering Science Advanced Research, Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN 37831
J. Barhen
Affiliation:
Center for Engineering Science Advanced Research, Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN 37831
Y. Braiman
Affiliation:
Center for Engineering Science Advanced Research, Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN 37831
Get access

Extract

We present a robust feedback control algorithm and apply it to the nonlinear oscillator array (Frenkel-Kontorova) model of nanoscale friction. The new control approach is based on the concepts of non-Lipschitzian dynamics and global targeting. We show that average quantities of the controlled system can be driven - exactly or approximately - towards desired targets which become additional, linearly stable attractors for the system's dynamics. Extensive numerical simulations show that the basins of attraction of these targets are reached in very short times and the control exhibits very strong robustness. We investigate the efficiency of the control in terms of various parameters (e.g., system size, non-Lipschitzian exponent).

Type
Research Article
Copyright
Copyright © Materials Research Society 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Heuberger, M., Drummond, C., and Israelachvili, J., J. Phys. Chem. B, 102, 5038 (1998).Google Scholar
2. Gao, J. P., Luedtke, W. D., and Landman, U., J. Phys. Chem. B, 102, 5033 (1998).Google Scholar
3. Rozman, M. G., Urbakh, M., and Klafter, J., Phys. Rev. Lett., 77, 683 (1996), Phys. Rev. E, 54, 6485 (1996).Google Scholar
4. Zaloj, V., Urbakh, M., and Klafter, J., Phys. Rev. Lett., 82, 4823 (1999).Google Scholar
5. Braiman, Y., Family, F., Hentschel, H. G. E., Mak, C., and Krim, J., Phys. Rev. E, 59, R4737 (1999).Google Scholar
6. Gao, J. P., Luedtke, W. D., and Landman, U., Tribol. Lett., 9, 3 (2000).Google Scholar
7. Braiman, Y., Barhen, J., and Protopopescu, V., Phys. Rev. Lett. 90, 094301 (2003)Google Scholar
8. Protopopescu, V. and Barhen, J., submitted to Chaos (2004).Google Scholar
9. Braiman, Y., Family, F., and Hentschel, H. G. E., Phys. Rev. E, 53, R3005 (1996).Google Scholar
10. Barhen, J., Gulati, S., and Zak, M., IEEE Computer, 22(6), 67 (1989).Google Scholar
11. Zak, M., Zbilut, J., and Meyers, R., From Instability to Intelligence, Springer, 1997.Google Scholar
12. Cover, A., Fryer, M., Lenhart, S., Protopopescu, V., and Reneke, J., Math. Models and Meth. in Appl. Sciences, 6, 77 (1996).Google Scholar
13. Amann, H. and Diaz, J. I., Nonlinear Analysis 55, 209 (2003).Google Scholar
14. Rozman, M. G., Urbakh, M., and Klafter, J., Phys. Rev. Lett., 77, 683 (1996).Google Scholar
15. M Carlson, J. and Batista, A. A., Phys. Rev. E, 53, 4153 (1996).Google Scholar
16. Persson, B. N. J. and Nitzan, A., Surf. Sci., 367, 261 (1996).Google Scholar
17. Yoshizawa, H., McGuiggan, P., and Israraelachvili, J., Science 259, 1305 (1993).Google Scholar
18. Reiter, G., Demirel, L., and Granick, S., Science 263, 1741 (1994).Google Scholar
19. Daly, C. and Krim, J., Phys. Rev. Lett. 76, 803 (1996).Google Scholar