Innovation-based subspace identification in open- and closed-loop
2016 IEEE 55th Conference on Decision and Control, CDC 2016, Page: 2951-2956
2016
- 11Citations
- 18Usage
- 16Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations11
- Citation Indexes11
- 11
- CrossRef1
- Usage18
- Abstract Views18
- Captures16
- Readers16
- 16
Conference Paper Description
The applicability of subspace-based system identification methods highly depends on the disturbances acting on the system. It is well-known, e.g., that the standard implementations of the MOESP, N4SID or CVA algorithms yield biased estimates when closed-loop noisy data is considered. In order to bypass this difficulty, we follow the recent trends for closed-loop subspace-based model identification and suggest, in a first step, pre-estimating the innovation term from the available data. By doing so, the initial subspace-based identification problem can be written as a deterministic problem for which efficient methods exist. Once the innovation sequence is estimated, the second step of our subspace-based identification procedure focuses on the estimation of the open-loop and closed-loop system's Markov parameters. A constrained least-squares solution is more precisely considered to guarantee structural constraints satisfied by Toeplitz matrices involved the open-loop and closed-loop data equations, respectively. The performance of the methods is illustrated through the study of simulation examples under open-loop and closed-loop conditions.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85010790055&origin=inward; http://dx.doi.org/10.1109/cdc.2016.7798709; http://ieeexplore.ieee.org/document/7798709/; http://xplorestaging.ieee.org/ielx7/7786694/7798233/07798709.pdf?arnumber=7798709; https://nsuworks.nova.edu/gscis_facarticles/418; https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1401&context=gscis_facarticles
Institute of Electrical and Electronics Engineers (IEEE)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know