Neural-network-based incremental backstepping sliding mode control for flying-wing aircraft

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dc.contributor.authorLiu, Shiqian
dc.contributor.authorLyu, Weizhi
dc.contributor.authorZhang, Qian
dc.contributor.authorYang, Congjie
dc.contributor.authorWhidborne, James F.
dc.date.accessioned2025-03-03T10:56:16Z
dc.date.available2025-03-03T10:56:16Z
dc.date.freetoread2025-03-03
dc.date.issued2025-03
dc.date.pubOnline2025-01-10
dc.description.abstractThe nonlinear trajectory tracking control problem is studied for a flying-wing aircraft. Starting from a nonlinear dynamics model of the flying-wing aircraft, the trajectory tracking control is decomposed into multiple loops of position control, flight path control, and attitude control. An incremental backstepping sliding mode control is proposed to implement attitude control, while an incremental nonlinear dynamic inversion and a nonlinear dynamic inversion design are used to deal with the nonlinear system model for the flight path and position control, respectively. In addition, a radial basis function neural-network-based extended state disturbance observer is proposed to deal with model uncertainties, gust disturbances, and unknown faults of the aircraft. The closed-loop control system is proved to be stable using Lyapunov theory. The performance of the proposed disturbance-observer-based incremental backstepping sliding mode control is demonstrated in simulation through a set of three-dimensional tracking scenarios. Compared with both backstepping control and backstepping sliding mode control, tracking performance measured by settling time, tracking error, and overshoot are improved by the proposed design when realistic trajectory tracking missions are executed.
dc.description.journalNameJournal of Guidance, Control, and Dynamics
dc.description.sponsorshipThe work described in this paper was supported by National Natural Science Foundation of China (52272400, 10577012).
dc.format.extent600-614
dc.identifier.citationLiu S, Lyu W, Zhang Q, et al., (2025) Neural-network-based incremental backstepping sliding mode control for flying-wing aircraft. Journal of Guidance, Control, and Dynamics, Volume 48, Issue 3, March 2025, pp. 600-614
dc.identifier.eissn1533-3884
dc.identifier.elementsID563577
dc.identifier.issn0731-5090
dc.identifier.issueNo3
dc.identifier.urihttps://doi.org/10.2514/1.g008215
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23554
dc.identifier.volumeNo48
dc.languageEnglish
dc.language.isoen
dc.publisherAIAA
dc.publisher.urihttps://arc.aiaa.org/doi/10.2514/1.G008215
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial Neural Network
dc.subjectTransport Aircraft
dc.subjectTracking Control
dc.subjectActive Disturbance Rejection Control
dc.subjectBackstepping Control
dc.subjectFault Tolerance
dc.subjectNonlinear Dynamic Inversion
dc.subjectFlying Wings
dc.subjectSliding Mode Control
dc.subject4007 Control Engineering, Mechatronics and Robotics
dc.subject40 Engineering
dc.subject4001 Aerospace Engineering
dc.subject4010 Engineering Practice and Education
dc.subjectAerospace & Aeronautics
dc.subject4001 Aerospace engineering
dc.subject4007 Control engineering, mechatronics and robotics
dc.subject4017 Mechanical engineering
dc.titleNeural-network-based incremental backstepping sliding mode control for flying-wing aircraft
dc.typeArticle
dc.type.subtypeArticle
dc.type.subtypeEarly Access
dcterms.dateAccepted2024-10-13

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