A Computational Method for the Nonlinear Attainable Moment Set of Tailless UAVs in Flight-Control-Oriented Scenarios

Tailless unmanned aerial vehicles (UAVs) achieve high-agility maneuvers with flight control systems. The attainable moment set (AMS) provides critical theoretical foundations and constraints for their optimization. A computational method is proposed herein to address controllability limitations caused by nonlinear aerodynamic effectiveness. This method incorporates dual constraints on control surface angles and angular rates for the nonlinear AMS, aiming to meet the demands of attitude tracking dynamics in flight control systems. First, a quantitative model is established to correlate dual deflection constraints with aerodynamic moment amplitude and bandwidth limitations. Next, we construct a computational framework for the incremental attainable moment set (IAMS) based on differential inclusion theory. For monotonic nonlinear aerodynamic effectiveness, the vertices of the IAMS are updated using local interpolation, yielding the incremental nonlinear attainable moment set (INAMS). When non-monotonic nonlinearity occurs, stationary points are calculated to adjust the control effectiveness matrix and admissible control set, thereby reducing computational errors induced by non-monotonic characteristics. Furthermore, the effective actions set, derived from a time-varying incremental nonlinear attainable moment set, quantifies the residual moment envelope of tailless UAVs during maneuvers. Comparative simulations indicate that the proposed method achieves correct computation under nonlinear aerodynamic conditions while reliably determining safe flight boundaries during control failure.

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Publication Details

Journal
Drones
Published
2025-08-18
DOI
https://doi.org/10.3390/drones9080585
Primary Topic
Aerospace Engineering and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

A Computational Method for the Nonlinear Attainable Moment Set of Tailless UAVs in Flight-Control-Oriented Scenarios

Jianbo Hu, Yingyang Wang, Linxiao Han, Peng Zhang et al.
Drones
Aerospace Engineering and Control Systems
article

A Computational Method for the Nonlinear Attainable Moment Set of Tailless UAVs in Flight-Control-Oriented Scenarios

Jianbo Hu, Yingyang Wang, Linxiao Han, Peng Zhang, Yuan Bian
article en

Abstract

Tailless unmanned aerial vehicles (UAVs) achieve high-agility maneuvers with flight control systems. The attainable moment set (AMS) provides critical theoretical foundations and constraints for their optimization. A computational method is proposed herein to address controllability limitations caused by nonlinear aerodynamic effectiveness. This method incorporates dual constraints on control surface angles and angular rates for the nonlinear AMS, aiming to meet the demands of attitude tracking dynamics in flight control systems. First, a quantitative model is established to correlate dual deflection constraints with aerodynamic moment amplitude and bandwidth limitations. Next, we construct a computational framework for the incremental attainable moment set (IAMS) based on differential inclusion theory. For monotonic nonlinear aerodynamic effectiveness, the vertices of the IAMS are updated using local interpolation, yielding the incremental nonlinear attainable moment set (INAMS). When non-monotonic nonlinearity occurs, stationary points are calculated to adjust the control effectiveness matrix and admissible control set, thereby reducing computational errors induced by non-monotonic characteristics. Furthermore, the effective actions set, derived from a time-varying incremental nonlinear attainable moment set, quantifies the residual moment envelope of tailless UAVs during maneuvers. Comparative simulations indicate that the proposed method achieves correct computation under nonlinear aerodynamic conditions while reliably determining safe flight boundaries during control failure.

DronesVol. 9(8)
Air Force Engineering University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 71%
Aerospace Engineering and Control Systems
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