An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation
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.
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The micro-randomized trial is a sequential factorial design for modeling effectiveness of treatment components over time.
“The micro-randomized trial is a sequential factorial design for modeling effectiveness of treatment components over time.”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [18]
The micro-randomized trial can be used to test proximal main effects of components over time, time-varying moderation, and, with variations in study design based on system identification, the development of idiographic computational models.
“It can be used to test proximal main effects of components over time, time-varying moderation [16], and with variations in study design based on strategies from system identification, the development of idiographic computational models [17, 18].”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [18]
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Agile science: creating useful products for behavior change in the real world
Hekler, E. B., Klasnja, P., Riley, W. T., Buman, M. P., Huberty, J., Rivera, D. E., & Martin, C. A. (2016) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0395-7 · Cited by 208 · see citing works
Self-citationMartin, C. A., Rivera, D. E., & Hekler, E. B. (2016) · 2016 American Control Conference (ACC) · doi:10.1109/acc.2016.7525468 · Cited by 20 · see citing works
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- Authors
- Martin, C. A., Rivera, D. E., & Hekler, E. B.
- Affiliations
- Arizona State University (US)
- Venue
- 2015 54th IEEE Conference on Decision and Control (CDC)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2719-2724
- ISSN
- 0191-2216 (ISSN-L)
- OpenAlex
- W2289697244
- Cited by
- 13
- References
- 14
- Citations by year
- 2025: 2 · 2023: 1 · 2022: 1 · 2020: 1 · 2018: 4 · 2016: 4
- FWCI
- 3.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Decision and Control (CDC) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- MIMO system identification, uncertainty regions, stopping rules, multivariable systems
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