Neural network based adaptive nonsingular practical predefined-time fault-tolerant control for hypersonic morphing aircraft
This paper develops a novel Neural Network (NN)-based adaptive nonsingular practical predefined-time controller for the hypersonic morphing aircraft subject to actuator faults. Firstly, a novel Lyapunov criterion of practical predefined-time stability is established. Following the proposed criterion...
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| Published in | Chinese journal of aeronautics Vol. 37; no. 4; pp. 421 - 435 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.04.2024
School of Astronautics,Harbin Institute of Technology,Harbin 150001,China%Beijing Institute of Control & Electronics Technology,Beijing 100038,China |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-9361 |
| DOI | 10.1016/j.cja.2023.12.020 |
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| Abstract | This paper develops a novel Neural Network (NN)-based adaptive nonsingular practical predefined-time controller for the hypersonic morphing aircraft subject to actuator faults. Firstly, a novel Lyapunov criterion of practical predefined-time stability is established. Following the proposed criterion, a tangent function based nonsingular predefined-time sliding manifold and the control strategy are developed. Secondly, the radial basis function NN with a low-complexity adaptation mechanism is incorporated into the controller to tackle the actuator faults and uncertainties. Thirdly, rigorous theoretical proof reveals that the attitude tracking errors can converge to a small region around the origin within a predefined time, while all signals in the closed-loop system remain bounded. Finally, numerical simulation results are presented to verify the effectiveness and improved performance of the proposed control scheme. |
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| AbstractList | This paper develops a novel Neural Network (NN)-based adaptive nonsingular practical predefined-time controller for the hypersonic morphing aircraft subject to actuator faults. Firstly, a novel Lyapunov criterion of practical predefined-time stability is established. Following the proposed criterion, a tangent function based nonsingular predefined-time sliding manifold and the control strategy are developed. Secondly, the radial basis function NN with a low-complexity adaptation mechanism is incorporated into the controller to tackle the actuator faults and uncertainties. Thirdly, rigorous theoretical proof reveals that the attitude tracking errors can converge to a small region around the origin within a predefined time, while all signals in the closed-loop system remain bounded. Finally, numerical simulation results are presented to verify the effectiveness and improved performance of the proposed control scheme. This paper develops a novel Neural Network(NN)-based adaptive nonsingular practical predefined-time controller for the hypersonic morphing aircraft subject to actuator faults.Firstly,a novel Lyapunov criterion of practical predefined-time stability is established.Following the pro-posed criterion,a tangent function based nonsingular predefined-time sliding manifold and the con-trol strategy are developed.Secondly,the radial basis function NN with a low-complexity adaptation mechanism is incorporated into the controller to tackle the actuator faults and uncer-tainties.Thirdly,rigorous theoretical proof reveals that the attitude tracking errors can converge to a small region around the origin within a predefined time,while all signals in the closed-loop sys-tem remain bounded.Finally,numerical simulation results are presented to verify the effectiveness and improved performance of the proposed control scheme. |
| Author | WEI, Changzhu ZHANG, Litao XU, Shihao MU, Rongjun |
| AuthorAffiliation | School of Astronautics,Harbin Institute of Technology,Harbin 150001,China%Beijing Institute of Control & Electronics Technology,Beijing 100038,China |
| AuthorAffiliation_xml | – name: School of Astronautics,Harbin Institute of Technology,Harbin 150001,China%Beijing Institute of Control & Electronics Technology,Beijing 100038,China |
| Author_xml | – sequence: 1 givenname: Shihao surname: XU fullname: XU, Shihao organization: School of Astronautics, Harbin Institute of Technology, Harbin 150001, China – sequence: 2 givenname: Changzhu surname: WEI fullname: WEI, Changzhu email: weichangzhu@hit.edu.cn organization: School of Astronautics, Harbin Institute of Technology, Harbin 150001, China – sequence: 3 givenname: Litao surname: ZHANG fullname: ZHANG, Litao organization: Beijing Institute of Control & Electronics Technology, Beijing 100038, China – sequence: 4 givenname: Rongjun surname: MU fullname: MU, Rongjun organization: School of Astronautics, Harbin Institute of Technology, Harbin 150001, China |
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| CitedBy_id | crossref_primary_10_1007_s11071_025_10900_2 crossref_primary_10_1016_j_engappai_2024_109962 crossref_primary_10_1038_s41598_025_86783_5 crossref_primary_10_1007_s10489_024_06219_x crossref_primary_10_1016_j_ast_2025_110102 |
| Cites_doi | 10.1007/s11431-018-9377-8 10.1016/j.ast.2017.08.013 10.1007/s11071-016-3196-0 10.1016/j.isatra.2022.02.003 10.1007/s12555-020-0694-0 10.1007/s11071-019-04834-9 10.1016/j.cja.2013.06.004 10.1016/j.cja.2021.09.013 10.1061/(ASCE)AS.1943-5525.0001047 10.1016/j.neucom.2021.08.069 10.1016/j.jfranklin.2022.10.002 10.1016/j.ast.2022.107985 10.1016/j.ast.2015.11.039 10.1016/j.ast.2021.107204 10.1016/B978-0-12-811318-9.00028-4 10.1016/j.cja.2019.03.029 10.1109/TAC.2020.2967555 10.1109/TMECH.2019.2906289 10.1016/j.ast.2022.107959 10.1016/j.ast.2020.106314 10.1016/j.conengprac.2020.104682 10.1049/cth2.12380 10.1016/j.ast.2019.05.063 10.1177/0020294019830434 10.1016/0893-6080(90)90005-6 10.1109/TAES.2022.3230633 10.1109/9.486648 10.1016/j.automatica.2010.02.018 10.1016/j.ins.2020.08.059 10.1109/TII.2019.2945861 10.1016/j.jfranklin.2022.05.011 10.1109/TAES.2022.3222294 10.1109/ACCESS.2021.3074912 10.1016/j.cja.2021.10.037 10.1007/s11432-015-5349-z 10.1007/s12555-020-0764-3 |
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| Keywords | Hypersonic morphing aircraft (HMA) Neural network (NN) Practical predefined-time control Fault-tolerant control Adaptive control Hypersonic morphing air-craft(HMA) Neural network(NN) |
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| SubjectTerms | Adaptive control Fault-tolerant control Hypersonic morphing aircraft (HMA) Neural network (NN) Practical predefined-time control |
| Title | Neural network based adaptive nonsingular practical predefined-time fault-tolerant control for hypersonic morphing aircraft |
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