Predictions of stability and control for a flying wing

Tomac, Maximilian; Stenfelt, Gloria

doi:10.1016/j.ast.2014.09.007

Cited by 31 publications

(10 citation statements)

References 7 publications

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“…C Ompared with conventional UAVs, flying wing UAVs adopt the blended-wing-body configuration and eliminate the empennage, therefore, they have more advantages in aerodynamic, structural and stealth performance, such as an extremely low radar cross-section (RCS) and a significant reduction in fuel consumption and aerodynamic noise [1], [2]. These benefits make the flying wing UAV a popular research direction in both military and civil fields [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Kriging Aerodynamic Modeling and Multi-Objective Control Allocation for Flying Wing UAVs With Morphing Trailing-Edge

Zhao

Yang

2021

IEEE Access

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Flexible skin and continuous deformable control surface are the basic of adaptive wing technology for future aircraft. This paper presents a continuous morphing trailing-edge and its control allocation method for the flying wing Unmanned Aerial Vehicle (UAV). Firstly, we apply the Kriging method to establish the aerodynamic model of the morphing trailing-edge, with the initial sample points generated by the non-uniform optimal Latin Hypercube Sampling (LHS). Then, based on the Kriging model, the multi-objective control allocation problem is converted into a standard optimization form. To solve such problem, we design a Comprehensive Multi-Objective Particle Swarm Optimization (C-MOPSO) algorithm and an improved Hierarchical MOPSO (H-MOPSO) algorithm, in which multiple optimization objectives are prioritized and hierarchically optimized using the PSO algorithm. As for performance analysis, an attitude angle tracking flight control system is established to validate the effectiveness of our proposed control allocation methods. Simulation results show that both the C-MOPSO and H-MOPSO methods have similar performance in attitude angle tracking, while H-MOPSO achieves better multi-objective allocation performance.INDEX TERMS Flying wing UAV, morphing trailing-edge, Kriging aerodynamic modeling, multiobjective control allocation, Particle Swarm Optimization.

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Section: Introductionmentioning

confidence: 99%

Kriging Aerodynamic Modeling and Multi-Objective Control Allocation for Flying Wing UAVs With Morphing Trailing-Edge

Zhao

Yang

2021

IEEE Access

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“…Current performance and operational requirements for low observable aircraft drive the design towards finless low aspect ratio flying wing configurations with a leading edge sweep between 45-60 degrees [1][2][3]. Yaw control is typically provided through laterally asymmetric deployment of drag-based aerodynamic control effectors in the form of spoilers or split elevons [4]. Whilst these devices are effective, there is a significant increase in required design effort and system complexity due to the strong pitch, roll and yaw coupling of these controls [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Control Allocation Method for Yaw Control of Tailless Aircraft

et al. 2020

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Tailless aircraft without vertical stabilisers typically use drag effectors in the form of spoilers or split flaps to generate control moments in yaw. This paper introduces a novel control allocation method by which full three-axis control authority can be achieved by the use of conventional lift effectors only, which reduces system complexity and control deflection required to achieve a given yawing moment. The proposed method is based on synthesis of control allocation modes that generate asymmetric profile and lift induced drag whilst maintaining the lift, pitching moment and rolling moment at the trim state. The method uses low order models for aerodynamic behaviour characterisation based on thin aerofoil theory, lifting surface methodology and ESDU datasheets and is applied to trapezoidal wings of varying sweep and taper. Control allocation modes are derived using the zero-sets of surrogate models for the characterised aerodynamic behaviours. Results are presented in the form of control allocations for a range of trimmed sideslip angles up to 10 degrees optimised for either maximum aerodynamic efficiency (minimum drag for a specific yawing moment) or minimum aggregate control deflection (as a surrogate observability metric). Outcomes for the two optimisation objectives are correlated in that minimum deflection solutions are always consistent with efficient ones. A configuration with conventional drag effector is used as a reference baseline. It is shown that, through appropriate allocation of lift based control effectors, a given yawing moment can be produced with up to a factor of eight less aggregate control deflection and up to 30% less overall drag compared to use of a conventional drag effector.

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“…The problem of the dynamic stability of the flying wing aircraft designed for passengers’ flights was investigated by D’Urso and Martinez-Val (2008). Results of the flight dynamics analysis of UAVs were presented by Tomac and Stenfelt (2014); Esteban (2001) and Goraj (2014). Moreover, a multi-disciplinary optimisation recently became very popular in the aircraft design because of high computers performance capacity; the flight dynamics properties could be a constrain in the multi-disciplinary optimisation of the unconventional aircraft (Mieloszyk and Goetzendorf-Grabowski, 2017).…”

Section: Introductionmentioning

confidence: 99%

A numerical study into the longitudinal dynamic stability of the tailless aircraft

Kwiek

2019

AEAT

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Purpose The purpose of this research is a study into a mathematical approach of a tailless aircraft dynamic stability analysis. This research is focused on investigation of influence of elevons (elevator) on stability derivatives and consequently on the aircraft longitudinal dynamic stability. The main research question is to determine whether this impact should be taken into account on the conceptual and preliminary stage of the analysis of the longitudinal dynamic stability. Design/methodology/approach Aerodynamic coefficients and longitudinal stability derivatives were computed by Panukl (panel methods). The analysis of the dynamic stability of the tailless aircraft was made by the Matlab code and SDSA package. Findings The main result of the research is a comparison of the dynamic stability of the tailless aircraft for different approaches, with and without the impact of elevator deflection on the trim drag and stability derivatives. Research limitations/implications This paper presents research that mostly should be considered on the preliminary stage of aircraft design and dynamic stability analysis. The impact of elevons deflection on the aircraft moment of inertia has been omitted. Practical implications The results of this research will be useful for the further design of small tailless unmanned aerial vehicles (UAVs). Originality/value This research reveals that in case of the analysis of small tailless UAVs, the impact of elevons deflection on stability derivatives is bigger than the impact of a Mach number. This impact should be taken into consideration, especially for a phugoid mode.

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Predictions of stability and control for a flying wing

Cited by 31 publications

References 7 publications

Kriging Aerodynamic Modeling and Multi-Objective Control Allocation for Flying Wing UAVs With Morphing Trailing-Edge

Kriging Aerodynamic Modeling and Multi-Objective Control Allocation for Flying Wing UAVs With Morphing Trailing-Edge

A Novel Control Allocation Method for Yaw Control of Tailless Aircraft

A numerical study into the longitudinal dynamic stability of the tailless aircraft

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