Robust online joint state/input/parameter estimation of linear systems Article Swipe
Jean-Sébastien Brouillon
,
Keith Moffat
,
Florian Dörfler
,
Giancarlo Ferrari‐Trecate
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.05663
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.05663
This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that are corrupted with non-Gaussian noise or outliers, which are commonly found in engineering applications. In particular, it combines recursive, alternating, and iteratively-reweighted least squares into a single, one-step algorithm, which solves the estimation problem online and benefits from the robustness of least-deviation regression methods. The convergence of the iterative method is formally guaranteed. Numerical experiments show the good performance of the estimation algorithm in presence of outliers and in comparison to state-of-the-art methods.
Related Topics
Concepts
Outlier
Least absolute deviations
Robustness (evolution)
Computer science
Robust regression
Convergence (economics)
Iteratively reweighted least squares
Gaussian
Algorithm
Mathematical optimization
Iterative method
Least trimmed squares
Estimation theory
Non-linear least squares
Mathematics
Regression
Artificial intelligence
Statistics
Gene
Chemistry
Economic growth
Quantum mechanics
Biochemistry
Physics
Economics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.05663
- https://arxiv.org/pdf/2204.05663
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4223423237
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4223423237Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2204.05663Digital Object Identifier
- Title
-
Robust online joint state/input/parameter estimation of linear systemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-12Full publication date if available
- Authors
-
Jean-Sébastien Brouillon, Keith Moffat, Florian Dörfler, Giancarlo Ferrari‐TrecateList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.05663Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2204.05663Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2204.05663Direct OA link when available
- Concepts
-
Outlier, Least absolute deviations, Robustness (evolution), Computer science, Robust regression, Convergence (economics), Iteratively reweighted least squares, Gaussian, Algorithm, Mathematical optimization, Iterative method, Least trimmed squares, Estimation theory, Non-linear least squares, Mathematics, Regression, Artificial intelligence, Statistics, Gene, Chemistry, Economic growth, Quantum mechanics, Biochemistry, Physics, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.which | 35, 57 |
| abstract_inverted_index.input, | 10 |
| abstract_inverted_index.linear | 14 |
| abstract_inverted_index.method | 4, 21, 77 |
| abstract_inverted_index.online | 18, 62 |
| abstract_inverted_index.solves | 58 |
| abstract_inverted_index.state, | 9 |
| abstract_inverted_index.jointly | 6 |
| abstract_inverted_index.problem | 61 |
| abstract_inverted_index.single, | 54 |
| abstract_inverted_index.squares | 51 |
| abstract_inverted_index.systems | 15 |
| abstract_inverted_index.benefits | 64 |
| abstract_inverted_index.combines | 45 |
| abstract_inverted_index.commonly | 37 |
| abstract_inverted_index.designed | 24 |
| abstract_inverted_index.fashion. | 19 |
| abstract_inverted_index.formally | 79 |
| abstract_inverted_index.methods. | 71, 100 |
| abstract_inverted_index.one-step | 55 |
| abstract_inverted_index.outliers | 94 |
| abstract_inverted_index.presence | 92 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.Numerical | 81 |
| abstract_inverted_index.algorithm | 90 |
| abstract_inverted_index.corrupted | 29 |
| abstract_inverted_index.iterative | 76 |
| abstract_inverted_index.outliers, | 34 |
| abstract_inverted_index.specially | 23 |
| abstract_inverted_index.algorithm, | 56 |
| abstract_inverted_index.comparison | 97 |
| abstract_inverted_index.estimating | 7 |
| abstract_inverted_index.estimation | 60, 89 |
| abstract_inverted_index.parameters | 12 |
| abstract_inverted_index.recursive, | 46 |
| abstract_inverted_index.regression | 70 |
| abstract_inverted_index.robustness | 67 |
| abstract_inverted_index.convergence | 73 |
| abstract_inverted_index.engineering | 40 |
| abstract_inverted_index.experiments | 82 |
| abstract_inverted_index.guaranteed. | 80 |
| abstract_inverted_index.particular, | 43 |
| abstract_inverted_index.performance | 86 |
| abstract_inverted_index.alternating, | 47 |
| abstract_inverted_index.measurements | 26 |
| abstract_inverted_index.non-Gaussian | 31 |
| abstract_inverted_index.applications. | 41 |
| abstract_inverted_index.least-deviation | 69 |
| abstract_inverted_index.state-of-the-art | 99 |
| abstract_inverted_index.iteratively-reweighted | 49 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |