Body-Freedom Flutter Suppression for a Flexible Flying-Wing Drone via Time-Delayed Control

Zou, Qitong; Huang, Rui; Hu, Haiyan

doi:10.2514/1.g006114

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…The tailless flying‐wing aircraft has received widespread attention because of the special aerodynamic configuration. Compared with their tailed counterparts, flying‐wing aircraft has more advantages in improving lift‐to‐drag ratio, extending aircraft range, and reducing the radar cross‐section (RCS) due to the adoption of blended‐wing‐body design and the elimination of vertical and horizontal tails 1‐3 . Special layout makes tailless flying‐wing aircraft very promising in both military and civilian fields, but also brings new challenges for flight control.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with their tailed counterparts, flying-wing aircraft has more advantages in improving lift-to-drag ratio, extending aircraft range, and reducing the radar cross-section (RCS) due to the adoption of blended-wing-body design and the elimination of vertical and horizontal tails. [1][2][3] Special layout makes tailless flying-wing aircraft very promising in both military and civilian fields, but also brings new challenges for flight control. The short moment arm and lack of horizontal tail may lead to the bad longitudinal stability.…”

Section: Introductionmentioning

confidence: 99%

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Distributed synchronization control for multiple tailless flying‐wing UAVs with predefined tracking accuracy and input nonlinearities

Zhou

Dong

Chen

et al. 2023

Intl J Robust & Nonlinear

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This article addresses the distributed adaptive synchronization tracking control problem for multiple flying-wing UAVs with predefined accuracy and unknown input nonlinearities. Considering that the multiple flying-wing unmanned aerial vehicles (UAVs) system is a large-scale nonlinear and strongly coupled system, the control schemes in existing literature which only consider longitudinal, lateral or attitude control may not be applicable in practical engineering. Thus, a full-state flying-wing UAV model containing both translational and rotational motions is established, whose dynamics are six-degree-of-freedom with twelve-state-variables. Furthermore, to handle the problem that the prior knowledge of the actuator nonlinearities of multi-UAV system are difficult to obtain, a more general input nonlinear model is established and the adaptive boundary estimation technique is developed. Finally, a performance-oriented controller is proposed by incorporating a series of smooth functions into adaptive neural control design and Lyapunov analysis. Theoretical analysis and simulation resultsshow that the presented control scheme can guarantee the satisfactory transient tracking error performance, and the tracking error converges to a user-defined interval ultimately. K E Y W O R D Sadaptive neural control, predefined accuracy, six-degree-of-freedom (6-DoF) flying-wing UAV, unknown input nonlinearities INTRODUCTIONThe tailless flying-wing aircraft has received widespread attention because of the special aerodynamic configuration.Compared with their tailed counterparts, flying-wing aircraft has more advantages in improving lift-to-drag ratio, extending aircraft range, and reducing the radar cross-section (RCS) due to the adoption of blended-wing-body design and the elimination of vertical and horizontal tails. [1][2][3] Special layout makes tailless flying-wing aircraft very promising in both military and civilian fields, but also brings new challenges for flight control. The short moment arm and lack of horizontal tail may lead to the bad longitudinal stability. Meanwhile, the cancellation of vertical tail results in poor lateralAbbreviations: 6-DoF, six-degree-of-freedom; UAVs, unmanned aerial vehicles

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed synchronization control for multiple tailless flying‐wing UAVs with predefined tracking accuracy and input nonlinearities

Zhou

Dong

Chen

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Therefore, nonlinear models of flexible aircraft can be approximated as a gridding-based linear parametervarying (LPV) model [14], which consists of a family of state-space representations linearized at different airspeeds (i.e., grid points or operating points). However, for most flutter control schemes, a controller is usually first designed at a chosen grid point and then verified at other grid points [15][16][17][18][19]. The single-point design method is not only a tedious process, it also cannot guarantee performance and even stability when the airspeed varies between grid points.…”

Section: Introductionmentioning

confidence: 99%

Oblique Projection-Based Modal Matching Algorithm for LPV Model Order Reduction of Aeroservoelastic Systems

Liu

Gao

et al. 2023

Aerospace

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An aeroservoelastic system can be described as a gridding-based linear parameter-varying (LPV) model, whose dynamic characteristics usually vary with the airspeed. Due to the high order of the system, it is necessary to perform order reduction on LPV models to overcome the control design challenges. However, when directly extending linear time-invariant (LTI) model order reduction technologies to the LPV system, states of the reduced-order LTI models generated separately at different grid points could be inconsistent. In this paper, a novel modal matching algorithm is proposed to solve the problem of state inconsistency by identifying the internal connection between the models at adjacent grid points. An oblique projection-based distance metric is defined to improve the reliability of the modal matching algorithm. The reduced-order LPV model constructed based on this method would have a high fidelity relative to the original model and a smooth interpolation performance between grid points. The proposed algorithm is applied to the X-56A aircraft, and numerical results show its effectiveness.

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Broad bandgap active metamaterials with optimal time-delayed control

Gao

Wang

2023

International Journal of Mechanical Sciences

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Body-Freedom Flutter Suppression for a Flexible Flying-Wing Drone via Time-Delayed Control

Cited by 6 publications

References 32 publications

Distributed synchronization control for multiple tailless flying‐wing UAVs with predefined tracking accuracy and input nonlinearities

Distributed synchronization control for multiple tailless flying‐wing UAVs with predefined tracking accuracy and input nonlinearities

Oblique Projection-Based Modal Matching Algorithm for LPV Model Order Reduction of Aeroservoelastic Systems

Broad bandgap active metamaterials with optimal time-delayed control

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