Neural network disturbance observer-based anti-saturation backstepping control for hypersonic vehicles.
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| Title: | Neural network disturbance observer-based anti-saturation backstepping control for hypersonic vehicles. |
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| Authors: | Wang, C.1 (AUTHOR), Feng, D.1,2 (AUTHOR), Zhao, J.3 (AUTHOR), Dai, P.1 (AUTHOR) daipei@xidian.edu.cn, Chen, T.1,2 (AUTHOR), Wang, X.2,3 (AUTHOR) |
| Source: | Aeronautical Journal. Apr2026, Vol. 130 Issue 1346, p1083-1106. 24p. |
| Abstract: | This study proposes a radial basis function neural network disturbance observer- (RBFNNDO) based anti-saturation backstepping controller for hypersonic vehicles with input saturations and multiple disturbances. Firstly, in response to the problem of 'exploding complexity' in backstepping controller, we adopt finite-time tracking differentiators (FTD), which realise higher tracking accuracy and tracking speed than those of the existing methods. Secondly, we develop multivariable neural network disturbance observers to estimate the lumped disturbances involving aerodynamic uncertainties and external disturbances, thereby improving the robustness of the proposed controller. Thirdly, in order to alleviate the input saturation and minimise the duration time, we use an adaptive fixed-time anti-saturation compensator (AFAC). The simulation results have proven that our proposed backstepping controller outperforms other existing methods in terms of control performance and saturation time. [ABSTRACT FROM AUTHOR] |
| Copyright of Aeronautical Journal is the property of Cambridge University Press 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: 193117997 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Neural network disturbance observer-based anti-saturation backstepping control for hypersonic vehicles. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+C%2E%22">Wang, C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+D%2E%22">Feng, D.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+J%2E%22">Zhao, J.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dai%2C+P%2E%22">Dai, P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> daipei@xidian.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+T%2E%22">Chen, T.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+X%2E%22">Wang, X.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Aeronautical+Journal%22">Aeronautical Journal</searchLink>. Apr2026, Vol. 130 Issue 1346, p1083-1106. 24p. – Name: Abstract Label: Abstract Group: Ab Data: This study proposes a radial basis function neural network disturbance observer- (RBFNNDO) based anti-saturation backstepping controller for hypersonic vehicles with input saturations and multiple disturbances. Firstly, in response to the problem of 'exploding complexity' in backstepping controller, we adopt finite-time tracking differentiators (FTD), which realise higher tracking accuracy and tracking speed than those of the existing methods. Secondly, we develop multivariable neural network disturbance observers to estimate the lumped disturbances involving aerodynamic uncertainties and external disturbances, thereby improving the robustness of the proposed controller. Thirdly, in order to alleviate the input saturation and minimise the duration time, we use an adaptive fixed-time anti-saturation compensator (AFAC). The simulation results have proven that our proposed backstepping controller outperforms other existing methods in terms of control performance and saturation time. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Aeronautical Journal is the property of Cambridge University Press 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: 10.1017/aer.2025.10121 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1083 Titles: – TitleFull: Neural network disturbance observer-based anti-saturation backstepping control for hypersonic vehicles. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, C. – PersonEntity: Name: NameFull: Feng, D. – PersonEntity: Name: NameFull: Zhao, J. – PersonEntity: Name: NameFull: Dai, P. – PersonEntity: Name: NameFull: Chen, T. – PersonEntity: Name: NameFull: Wang, X. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00019240 Numbering: – Type: volume Value: 130 – Type: issue Value: 1346 Titles: – TitleFull: Aeronautical Journal Type: main |
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