Identification of an electromagnetic actuator

Proceedings of the IEEE Conference on Decision and Control

Volumn 4, Issue , 2002, Pages 4521-4526

  Lacy, Seth L ^a   Bernstein, Dennis S ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCELERATION MEASUREMENT; ACCELEROMETERS; ACTUATORS; DYNAMICS; LINEAR MOTORS; PERMANENT MAGNETS; POSITION MEASUREMENT; SENSORS; STEP RESPONSE; TIME DOMAIN ANALYSIS;

ELECTROMAGNETIC ACTUATOR; TIME-DOMAIN SYSTEM IDENTIFICATION TECHNIQUES;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 0036991443     PISSN: 01912216     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. F. Ahmed-Zaid, P. A. Ioannou, and M. M. Polycarpou. Identification and control of aircraft dynamics using radial basis function networks. In Second IEEE Confernce on Control Applications, pages 567-572, Vancouver, BC, September 1993.
2. E.-W. Bai. An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems. Automatica, 34(3):333-338, 1998.
3. D. Bauer and B. Ninness. Asymptotic properties of least-squares estimates of Hammerstein-Wiener models. International Journal of Control, 75(1):34-51, 2002.
4. D. S. Bayard and M. Eslami. Parameter identification of linear systems using nonlinear noninvertible measurements. In Proceedings of the 23rd Conference on Decision and Control, pages 348-352, Las Vegas, NV, December 1984.
5. D. R. Brillinger. The identification of polynomial systems by means of higher order spectra. Journal of Sound and Vibration, 12(3):301-313, 1970.
6. C. H. Chen and S. D. Fassois. Maximum likelihood identification of stochastic Wiener-Hammerstein-type non-linear systems. Mechanical Systems and Signal Processing, 6(2):135-153, 1992.
7. S. Chen and S. A. Billings. Neural networks for nonlinear dynamic system modelling and identification. International Journal of Control, 56(2):319-346, 1992.
8. S. Chen, S. A. Billings, C. F. N. Cowan, and P. M. Grant. Non-linear systems identification using radial basis functions. International Journal of Systems Science, 21(12):2513-2539, 1990.
9. S. Chen, S. A. Billings, and P. M. Grant. Nonlinear system identification using neural networks. International Journal of Control, 51(6):1191-1214, 1990.
10. H. E. Emara-Shabaik, K. A. F. Moustafa, and J. H. S. Talaq. On identification of parallel block-cascade nonlinear models. International Journal of Systems Science, 26(7):1429-1438, 1995.
11. W. Greblicki. Nonparametric identification of Wiener systems by orthogonal series. IEEE Transactions on Automatic Control, 39(10):2077-2086, 1994.
12. W. Greblicki. Nonparametric approach to Wiener system identification. IEEE Transactions on Circuits and Systems - I: Fundamental Theory and Applications, 44(6):538-545, 1997.
13. R. Haber and L. Keviczky. Nonlinear System Identification - Input-Output Modeling Approach, volume 1: Nonlinear System Parameter Identification. Kluwer Academic Publishers, 1999.
14. R. Haber and L. Keviczky. Nonlinear System Identification - Input-Output Modeling Approach, volume 2: Nonlinear System Structure Identification. Kluwer Academic Publishers, 1999.
15. Z. Hasiewicz. Identification of a linear system observed through zero-memory non-linearity. International Journal of Systems Science, 18(9):1595-1607, 1987.
16. I. W. Hunter and M. J. Korenberg. The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biological Cybernetics, 55:135-144, 1986.
17. Juditsky et al. Nonlinear black-box models in system identification: Mathematical foundations. Automatica, 31(12):1725-1750, 1995.
18. M. J. Korenberg and I. W. Hunter. The identification of nonlinear biological systems: LNL cascade models. Biological Cybernetics, 55:125-134, 1986.
19. S. L. Lacy and D. S. Bernstein. Subspace identification for nonlinear systems that are linear in unmeasured states. In Proceedings of the Conference on Decision and Control, Orlando, Florida, December 2001.
20. S. L. Lacy and D. S. Bernstein. Identification of FIR Wiener systems with unknown, noninvertible, polynomial nonlinearities. In Proceedings of the American Control Conference, Anchorage, Alaska, May 2002.
21. S. L. Lacy and D. S. Bernstein. Subspace identification with guaranteed stability using constrained optimization. In Proceedings of the American Control Conference, Anchorage, Alaska, May 2002.
22. S. L. Lacy, R. S. Erwin, and D. S. Bernstein. Identification of Wiener systems with known noninvertible nonlinearities. Journal of Dynamic Systems, Measurement, and Control, 123:1-6, December 2001.
23. M. Lovera, T. Gustafsson, and M. Verhaegen. Recursive subspace identification of linear and nonlinear Wiener state-space models. Automatica, 36:1639-1650, 2000.
24. K. S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1):4-27, 1990.
25. O. Nelles. Nonlinear System Identification. Springer, 2001.
26. G. A. Pajunen. Recursive identification of Wiener type nonlinear systems. In Proceedings of the 1985 American Control Conference, volume 3, pages 1365-1370, Boston, MA, June 1985.
27. Sjöberg et al. Nonlinear black-box modeling in system identification: A unified overview. Automatica, 31(12):1691-1724, 1995.
28. T. Van Pelt and D. S. Bernstein. Nonlinear system identification using hammerstein and nonlinear feedback models with piecewise linear static maps - Part I: Theory - Part II: Numerical examples. In Proceedings of the ACC, pages 225-229, 235-239, Chicago, IL, June 2000.
29. M. Verhaegen and D. Westwick. Identifying MIMO Hammerstein systems in the context of subspace model identification methods. International Journal of Control, 63(2):331-349, 1996.
30. D. Westwick and M. Verhaegen. Identifying MIMO Wiener systems using subspace model identification methods. Signal Processing, 52:235-258, 1996.
31. D. T. Westwick and R. E. Kearney. A new algorithm for the identification of multiple input Wiener systems. Biological Cybernetics, 68:75-85, 1992.
32. T. Wigren. Convergence analysis of recursive identification algorithms based on the Wiener model. IEEE Transactions on Automatic Control, 39(11):2191-2206, 1994.