On ANN-enhanced positive invariance for nonlinear flat systems.
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| Title: | On ANN-enhanced positive invariance for nonlinear flat systems. |
|---|---|
| Authors: | Do, Huu-Thinh1,2 (AUTHOR) thinh.doh04@gmail.com, Prodan, Ionela1 (AUTHOR) ionela.prodan@lcis.grenoble-inp.fr |
| Source: | Neural Computing & Applications. Apr2026, Vol. 38 Issue 7, p1-18. 18p. |
| Abstract: | The concept of positively invariant (PI) sets has proven effective in the formal verification of stability and safety properties for autonomous systems. However, the characterization of such sets is challenging for nonlinear systems in general, especially in the presence of constraints. In this work, we show that, for a class of feedback linearizable systems, called differentially flat systems, a PI set can be derived by leveraging a neural network approximation of the linearizing mapping. More specifically, for the class of flat systems, there exists a linearizing variable transformation that converts the nonlinear system into linear controllable dynamics, albeit at the cost of distorting the constraint set. We show that by approximating the distorted set using a rectified linear unit neural network, we can derive a PI set inside the admissible domain through its set-theoretic description. This offline characterization enables the synthesis of various efficient online control strategies, with different complexities and performances. Numerical simulations are provided to demonstrate the validity of the proposed framework. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192758042 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: On ANN-enhanced positive invariance for nonlinear flat systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Do%2C+Huu-Thinh%22">Do, Huu-Thinh</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> thinh.doh04@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Prodan%2C+Ionela%22">Prodan, Ionela</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ionela.prodan@lcis.grenoble-inp.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Apr2026, Vol. 38 Issue 7, p1-18. 18p. – Name: Abstract Label: Abstract Group: Ab Data: The concept of positively invariant (PI) sets has proven effective in the formal verification of stability and safety properties for autonomous systems. However, the characterization of such sets is challenging for nonlinear systems in general, especially in the presence of constraints. In this work, we show that, for a class of feedback linearizable systems, called differentially flat systems, a PI set can be derived by leveraging a neural network approximation of the linearizing mapping. More specifically, for the class of flat systems, there exists a linearizing variable transformation that converts the nonlinear system into linear controllable dynamics, albeit at the cost of distorting the constraint set. We show that by approximating the distorted set using a rectified linear unit neural network, we can derive a PI set inside the admissible domain through its set-theoretic description. This offline characterization enables the synthesis of various efficient online control strategies, with different complexities and performances. Numerical simulations are provided to demonstrate the validity of the proposed framework. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-026-11928-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Titles: – TitleFull: On ANN-enhanced positive invariance for nonlinear flat systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Do, Huu-Thinh – PersonEntity: Name: NameFull: Prodan, Ionela IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 38 – Type: issue Value: 7 Titles: – TitleFull: Neural Computing & Applications Type: main |
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