The informativity approach to data-driven analysis and control Article Swipe
Henk J. van Waarde
,
Jaap Eising
,
M. Kanat Camlibel
,
Harry L. Trentelman
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.10488
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.10488
The goal of this paper is to provide a tutorial on the so-called informativity framework for direct data-driven analysis and control. This framework achieves certified data-based analysis and control by assessing system properties and determining controllers for sets of systems unfalsified by the data. We will first introduce the informativity approach at an abstract level. Thereafter, we will report case studies where we highlight the strength of the framework in the context of various problems involving both noiseless and noisy data. In particular, we will treat controllability and stabilizability, and stabilization, linear quadratic regulation, and tracking and regulation using exact input-state measurements. Thereafter, we will treat dissipativity analysis, stabilization, and H_inf control using noisy input-state data. Finally, we will study dynamic measurement feedback stabilization using noisy input-output data. We will provide several examples to illustrate the approach. In addition, we will highlight the main tools underlying the framework, such as quadratic matrix inequalities in robust control and quadratic difference forms in behavioral systems theory.
Related Topics
Concepts
Controllability
Context (archaeology)
Computer science
Controller (irrigation)
Control theory (sociology)
State (computer science)
Control (management)
Quadratic equation
Robust control
Control system
Algorithm
Mathematics
Artificial intelligence
Engineering
Biology
Paleontology
Geometry
Applied mathematics
Agronomy
Electrical engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.10488
- https://arxiv.org/pdf/2302.10488
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4321593182
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4321593182Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.10488Digital Object Identifier
- Title
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The informativity approach to data-driven analysis and controlWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-21Full publication date if available
- Authors
-
Henk J. van Waarde, Jaap Eising, M. Kanat Camlibel, Harry L. TrentelmanList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.10488Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.10488Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2302.10488Direct OA link when available
- Concepts
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Controllability, Context (archaeology), Computer science, Controller (irrigation), Control theory (sociology), State (computer science), Control (management), Quadratic equation, Robust control, Control system, Algorithm, Mathematics, Artificial intelligence, Engineering, Biology, Paleontology, Geometry, Applied mathematics, Agronomy, Electrical engineeringTop 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.noiseless | 77 |
| abstract_inverted_index.quadratic | 92, 150, 157 |
| abstract_inverted_index.so-called | 12 |
| abstract_inverted_index.behavioral | 161 |
| abstract_inverted_index.data-based | 25 |
| abstract_inverted_index.difference | 158 |
| abstract_inverted_index.framework, | 147 |
| abstract_inverted_index.illustrate | 134 |
| abstract_inverted_index.properties | 32 |
| abstract_inverted_index.regulation | 97 |
| abstract_inverted_index.underlying | 145 |
| abstract_inverted_index.Thereafter, | 55, 102 |
| abstract_inverted_index.controllers | 35 |
| abstract_inverted_index.data-driven | 17 |
| abstract_inverted_index.determining | 34 |
| abstract_inverted_index.input-state | 100, 114 |
| abstract_inverted_index.measurement | 121 |
| abstract_inverted_index.particular, | 82 |
| abstract_inverted_index.regulation, | 93 |
| abstract_inverted_index.unfalsified | 40 |
| abstract_inverted_index.inequalities | 152 |
| abstract_inverted_index.input-output | 126 |
| abstract_inverted_index.dissipativity | 106 |
| abstract_inverted_index.informativity | 13, 49 |
| abstract_inverted_index.measurements. | 101 |
| abstract_inverted_index.stabilization | 123 |
| abstract_inverted_index.stabilization, | 90, 108 |
| abstract_inverted_index.controllability | 86 |
| abstract_inverted_index.stabilizability, | 88 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |