Using a neural network concept, an algorithm has been developed that can be employed to conduct on-line identification of modal parameters of a structure such as natural frequency, damping ratio, and mode shape coefficients at measurement locations. Using a band-pass filter, the algorithm extracts from the measurement signal the frequency contents in the vicinity of a desired mode. The filtered signal is then used to train a neural network which consists of a linear neuron with three weights. The structure of the neural network has been designed to allow direct identification of modal parameters from the weights and to enhance efficiency for on-line implementation. The algorithm has been implemented on a DSP (digital signal processor) system for performance evaluation. Modal parameter identification tests have been conducted for a laboratory circular plate structure. The algorithm runs approximately at a sampling frequency of up to 50 kHz for the DSP system used for the study and correctly identifies modal properties of the first two modes of the circular plate. This high performance on-line identification algorithm is expected to be useful in improving the performance of control systems that require knowledge of time-varying plant dynamic characteristics.

Issue Section:
Research Papers
Topics:
Artificial neural networks, Algorithms, Signals, Control systems, Damping, Filters, Mode shapes, Performance evaluation, Plates (structures), Trains
1.
Brown, D. E., and Cabell, R. H., 1993, “An Application of Filtered-X Techniques and Neural Networks to the Active Control of Non-Linear Systems,” Proceedings of Second Conference on Recent Advances in Active Control of Sound and Vibration,” Virginia Polytechnic Institute and State University, Blacksburg, VA, pp. 3–14.
2.
Chen, Chi-Tsong, 1984, Linear System Theory and Design, Holt, Reinhart and Winston, New York.
3.
Juang
J. N.
, and
Pappa
R. S.
,
1985
, “
An Eigensystem Realization Algorithm for Modal Parameter Identification and Model Reduction
,”
Journal of Guidance, Control and Dynamics
, Vol.
8
, No.
5
, pp.
620
–
627
.
4.
Juang
J.-N.
,
Phan
M.
,
Horta
L. G.
, and
Longman
R. W.
,
1993
, “
Identification of Observer and Kalman Filter Markov Parameters: Theory and Experiments
,”
Journal of Guidance, Control and Dynamics
, Vol.
16
, No.
2
, pp.
320
–
329
.
5.
Kuschewski
J. G.
,
Hui
S.
, and
Stanislaw
H. Z.
,
1993
, “
Application of Feedforward Neural Networks to Dynamical System Identification and Control
,”
IEEE Trans. on Control Systems Technology
, Vol.
1
, No.
1
, pp.
37
–
49
.
6.
Nelson, P. A., and Elliott, S. J., 1992, Active Control of Sound, Academic Press, San Diego, CA.
7.
Phan, M., Juang, J.-N., and Hyland, D. C., 1993, “On Neural Networks in Identification and Control of Dynamic Systems,” NASA TM 107702.
8.
Widrow, B., and Stearns, S. D., 1985, Adaptive Signal Processing, Prentice Hall, Englewood Cliffs, NJ.
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