TY - GEN
T1 - An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation
AU - Martin, Cesar A.
AU - Rivera, Daniel
AU - Hekler, Eric B.
N1 - Funding Information:
VII. ACKNOWLEDGMENTS Support for this work has been provided by the National Science Foundation (NSF) through grant IIS-1449751. The opinions expressed in this article are the authors’ own and do not necessarily reflect the views of NSF.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/8
Y1 - 2015/2/8
N2 - This paper presents an identification test monitoring procedure for multivariable systems whose purpose is to define an experiment that is both sufficiently informative for identification purposes and of the shortest duration possible, given predefined levels of accuracy in the model. The procedure relies on uncertainty regions resulting from frequency-domain transfer function estimation that is performed during experimental execution. To obtain independent-in-frequency signals for estimation, input design relying on multi-sinusoidal signals with zippered power spectra is developed. Given the various approaches available for computing statistically-based uncertainty in the frequency domain, the method that offers the most general conditions with the least a priori information about the output noise structure is selected. Based on the computed uncertainties and user-defined bounds, a stopping criterion for the identification test is developed. Results are evaluated with a simulation study involving a representative process model. This includes a performance evaluation of the technique under various distinct noise models.
AB - This paper presents an identification test monitoring procedure for multivariable systems whose purpose is to define an experiment that is both sufficiently informative for identification purposes and of the shortest duration possible, given predefined levels of accuracy in the model. The procedure relies on uncertainty regions resulting from frequency-domain transfer function estimation that is performed during experimental execution. To obtain independent-in-frequency signals for estimation, input design relying on multi-sinusoidal signals with zippered power spectra is developed. Given the various approaches available for computing statistically-based uncertainty in the frequency domain, the method that offers the most general conditions with the least a priori information about the output noise structure is selected. Based on the computed uncertainties and user-defined bounds, a stopping criterion for the identification test is developed. Results are evaluated with a simulation study involving a representative process model. This includes a performance evaluation of the technique under various distinct noise models.
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U2 - 10.1109/CDC.2015.7402627
DO - 10.1109/CDC.2015.7402627
M3 - Conference contribution
AN - SCOPUS:84962019021
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 2719
EP - 2724
BT - 54rd IEEE Conference on Decision and Control,CDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th IEEE Conference on Decision and Control, CDC 2015
Y2 - 15 December 2015 through 18 December 2015
ER -