An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation

Cesar A. Martin, Daniel Rivera, Eric B. Hekler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publication2015 54th IEEE Conference on Decision and Control, CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2719-2724
Number of pages6
Volume2016-February
ISBN (Electronic)9781479978861
DOIs
StatePublished - Feb 8 2016
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015

Other

Other54th IEEE Conference on Decision and Control, CDC 2015
CountryJapan
CityOsaka
Period12/15/1512/18/15

Fingerprint

Uncertainty Estimation
Statistical Estimation
MIMO Systems
MIMO systems
Monitoring
Uncertainty
Frequency Domain
Multivariable systems
Stopping Criterion
Function Estimation
Multivariable Systems
Power spectrum
Power Spectrum
Transfer Function
Process Model
Transfer functions
Performance Evaluation
Identification (control systems)
Simulation Study
Distinct

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Martin, C. A., Rivera, D., & Hekler, E. B. (2016). An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation. In 2015 54th IEEE Conference on Decision and Control, CDC 2015 (Vol. 2016-February, pp. 2719-2724). [7402627] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2015.7402627

An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation. / Martin, Cesar A.; Rivera, Daniel; Hekler, Eric B.

2015 54th IEEE Conference on Decision and Control, CDC 2015. Vol. 2016-February Institute of Electrical and Electronics Engineers Inc., 2016. p. 2719-2724 7402627.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Martin, CA, Rivera, D & Hekler, EB 2016, An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation. in 2015 54th IEEE Conference on Decision and Control, CDC 2015. vol. 2016-February, 7402627, Institute of Electrical and Electronics Engineers Inc., pp. 2719-2724, 54th IEEE Conference on Decision and Control, CDC 2015, Osaka, Japan, 12/15/15. https://doi.org/10.1109/CDC.2015.7402627
Martin CA, Rivera D, Hekler EB. An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation. In 2015 54th IEEE Conference on Decision and Control, CDC 2015. Vol. 2016-February. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2719-2724. 7402627 https://doi.org/10.1109/CDC.2015.7402627
Martin, Cesar A. ; Rivera, Daniel ; Hekler, Eric B. / An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation. 2015 54th IEEE Conference on Decision and Control, CDC 2015. Vol. 2016-February Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2719-2724
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