Control Authority of a Camber Morphing Flying Wing

Keidel, Dominic; Fasel, Urban; Ermanni, Paolo

doi:10.2514/1.c035606

Cited by 13 publications

(12 citation statements)

References 36 publications

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“…According to the selected initial sample points, we choose the three-dimensional (3D) panel method to evaluate the aerodynamic characteristics of the morphing trailing-edge flying wing UAV under various actuator deflections. The 3D panel method is an aerodynamic analysis tool with high computational efficiency, and has sufficient accuracy to evaluate the trends of aerodynamic coefficients [8]. Specifically, we choose XFLR5 software 1 embedded with 3D panel method as the aerodynamic analysis tool.…”

Section: A Design Of Experimentsmentioning

confidence: 99%

“…In the current researches, Wang et al investigated the control problem of morphing wings based on switched nonlinear systems and adaptive dynamic programming [7]. The author of [8] systematically studied the control authority of camber morphing flying wings. The main challenge in the design of the control system for flying wing UAVs with camber morphing is the control redundancy and multi-axis coupling.…”

Section: Introductionmentioning

confidence: 99%

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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: A Design Of Experimentsmentioning

confidence: 99%

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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“…Thus, numerical aerodynamic shape optimization is a fast and efficient method to improve aerodynamic performance of flying wing aircraft [16]. Recently, aerodynamic shape optimization in flying wing aircraft design mainly focused on lift-drag ratio improvement [17,18], drag reduction in a cruise state [19][20][21][22], operating cost reduction [23], and stealth performance improvement [24]. Some optimizations were conducted based on the surrogate model [17,18].…”

Section: Introductionmentioning

confidence: 99%

Study on a Rapid Aerodynamic Optimization Method of Flying Wing Aircraft for Conceptual Design

Wei

Huang

Song

2022

International Journal of Aerospace Engineering

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Searching for a design scheme satisfying the requirements in aircraft conceptual design can be a time-consuming work because of the multipeak and nonlinearity of the design space. This paper proposed a rapid aerodynamic optimization method for flying wing aircraft conceptual design. This method is aimed at reducing the induced drag at the design point by adjusting the camber and twist angle of spanwise airfoil. Firstly, the mean camber surface of the flying wing aircraft was parameterized. Secondly, the surrogate model was constructed based on the points selected by the optimization Latin square method. Thirdly, the surrogate model combined with a multi-island genetic algorithm was used for the preliminary solution of global optimum, and then, the Nonlinear Programming by Quadratic Lagrangian method combined with a vortex lattice method was used for searching the nearest exact best point from the initial best point. Finally, connecting with manual selection, the optimized flying wing layout scheme was obtained. The optimized results show that the induced drag coefficient is reduced by 10%, the pitching moment coefficient is reduced by an order of magnitude, and the lift drag ratio is increased from 26.3 to 27.3. The proposed optimization method decreases the time cost in aircraft conceptual design while achieving sufficient calculation accuracy. By using this method, the design space can be explored rapidly to search for the best design scheme satisfying the constraints.

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“…Modelling of AWE morphing drones: I . Exemplary AWE morphing drone (designed, manufactured and tested within the ftero AWE project at ETH Zurich [33,34]), exploiting camber-morphing for roll-control [13,35,36]. II .…”

Section: Introductionmentioning

confidence: 99%

Data-driven nonlinear aeroelastic models of morphing wings for control

2020

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Accurate and efficient aeroelastic models are critically important for enabling the optimization and control of highly flexible aerospace structures, which are expected to become pervasive in future transportation and energy systems. Advanced materials and morphing wing technologies are resulting in next-generation aeroelastic systems that are characterized by highly coupled and nonlinear interactions between the aerodynamic and structural dynamics. In this work, we leverage emerging data-driven modelling techniques to develop highly accurate and tractable reduced-order aeroelastic models that are valid over a wide range of operating conditions and are suitable for control. In particular, we develop two extensions to the recent dynamic mode decomposition with control (DMDc) algorithm to make it suitable for flexible aeroelastic systems: (1) we introduce a formulation to handle algebraic equations, and (2) we develop an interpolation scheme to smoothly connect several linear DMDc models developed in different operating regimes. Thus, the innovation lies in accurately modelling the nonlinearities of the coupled aerostructural dynamics over multiple operating regimes, not restricting the validity of the model to a narrow region around a linearization point. We demonstrate this approach on a high-fidelity, three-dimensional numerical model of an airborne wind energy system, although the methods are generally applicable to any highly coupled aeroelastic system or dynamical system operating over multiple operating regimes. Our proposed modelling framework results in real-time prediction of nonlinear unsteady aeroelastic responses of flexible aerospace structures, and we demonstrate the enhanced model performance for model predictive control. Thus, the proposed architecture may help enable the widespread adoption of next-generation morphing wing technologies.

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Control Authority of a Camber Morphing Flying Wing

Cited by 13 publications

References 36 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

Study on a Rapid Aerodynamic Optimization Method of Flying Wing Aircraft for Conceptual Design

Data-driven nonlinear aeroelastic models of morphing wings for control

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