Data-Enabled Identification of Nonlinear Dynamics of Water Systems using Sparse Regression Technique Article Swipe
Faegheh Moazeni
,
Javad Khazaei
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2023.10.1212
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2023.10.1212
The complex, multi-variable, highly nonlinear and strong coupling characteristics of water distribution systems (WDSs) has significantly limited the capability of model-based approaches for control purposes in such systems. With the emerging application of high-resolution metering devices and historical data, model-free identification of WDSs can facilitate the control design without tedious modeling complexities. This paper develops a data-driven framework to facilitate the identification of nonlinear models of WDSs using available data. A quadruple tank system that represents the nonlinear and strong coupling nature of WDSs is considered as the test system and sparse identification of nonlinear dynamics (SINDy) is utilized to identify the nonlinear dynamics from the data. Unlike existing modeling approaches that either heavily rely on knowing the detailed dynamics of the system (model-based) or designs that relay on large historical data and are not interpretable (data-driven approaches), the proposed model-free identification framework is parsimonious, which can accurately capture the dynamics of the quadruple tank process with available measurements suitable for control problems. The effectiveness of the proposed approach is validated using time-domain simulations in MATLAB.
Related Topics
Concepts
Nonlinear system
Identification (biology)
Computer science
System dynamics
System identification
Nonlinear system identification
Process (computing)
MATLAB
Data mining
Control engineering
Engineering
Artificial intelligence
Biology
Botany
Operating system
Physics
Measure (data warehouse)
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2023.10.1212
- OA Status
- diamond
- Cited By
- 3
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388903951
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4388903951Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ifacol.2023.10.1212Digital Object Identifier
- Title
-
Data-Enabled Identification of Nonlinear Dynamics of Water Systems using Sparse Regression TechniqueWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Faegheh Moazeni, Javad KhazaeiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ifacol.2023.10.1212Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ifacol.2023.10.1212Direct OA link when available
- Concepts
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Nonlinear system, Identification (biology), Computer science, System dynamics, System identification, Nonlinear system identification, Process (computing), MATLAB, Data mining, Control engineering, Engineering, Artificial intelligence, Biology, Botany, Operating system, Physics, Measure (data warehouse), Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.limited | 16 |
| abstract_inverted_index.process | 155 |
| abstract_inverted_index.systems | 12 |
| abstract_inverted_index.tedious | 49 |
| abstract_inverted_index.without | 48 |
| abstract_inverted_index.approach | 168 |
| abstract_inverted_index.complex, | 1 |
| abstract_inverted_index.coupling | 7, 80 |
| abstract_inverted_index.detailed | 118 |
| abstract_inverted_index.develops | 54 |
| abstract_inverted_index.dynamics | 95, 103, 119, 150 |
| abstract_inverted_index.emerging | 30 |
| abstract_inverted_index.existing | 108 |
| abstract_inverted_index.identify | 100 |
| abstract_inverted_index.metering | 34 |
| abstract_inverted_index.modeling | 50, 109 |
| abstract_inverted_index.proposed | 139, 167 |
| abstract_inverted_index.purposes | 24 |
| abstract_inverted_index.suitable | 159 |
| abstract_inverted_index.systems. | 27 |
| abstract_inverted_index.utilized | 98 |
| abstract_inverted_index.available | 68, 157 |
| abstract_inverted_index.framework | 57, 142 |
| abstract_inverted_index.nonlinear | 4, 63, 77, 94, 102 |
| abstract_inverted_index.problems. | 162 |
| abstract_inverted_index.quadruple | 71, 153 |
| abstract_inverted_index.validated | 170 |
| abstract_inverted_index.accurately | 147 |
| abstract_inverted_index.approaches | 21, 110 |
| abstract_inverted_index.capability | 18 |
| abstract_inverted_index.considered | 85 |
| abstract_inverted_index.facilitate | 44, 59 |
| abstract_inverted_index.historical | 37, 130 |
| abstract_inverted_index.model-free | 39, 140 |
| abstract_inverted_index.represents | 75 |
| abstract_inverted_index.application | 31 |
| abstract_inverted_index.data-driven | 56 |
| abstract_inverted_index.model-based | 20 |
| abstract_inverted_index.simulations | 173 |
| abstract_inverted_index.time-domain | 172 |
| abstract_inverted_index.(data-driven | 136 |
| abstract_inverted_index.approaches), | 137 |
| abstract_inverted_index.distribution | 11 |
| abstract_inverted_index.measurements | 158 |
| abstract_inverted_index.(model-based) | 123 |
| abstract_inverted_index.complexities. | 51 |
| abstract_inverted_index.effectiveness | 164 |
| abstract_inverted_index.interpretable | 135 |
| abstract_inverted_index.parsimonious, | 144 |
| abstract_inverted_index.significantly | 15 |
| abstract_inverted_index.identification | 40, 61, 92, 141 |
| abstract_inverted_index.characteristics | 8 |
| abstract_inverted_index.high-resolution | 33 |
| abstract_inverted_index.multi-variable, | 2 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5007059001, https://openalex.org/A5041147963 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I186143895 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.63976142 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |