Tailless aircraft suffers from limited yaw control power and poor directional stability inherently. To address these issues in the early design process of a tailless configuration with low costs and risks, this paper presents an innovative experimental approach to control law validation and quantitative flying quality evaluation with a dynamically scaled model mounted on a three degree-of-freedom rig in the wind tunnel. The motion equations of the tailless demonstrator on the rig are derived, and then the comparisons of the lateral-directional flight dynamics between the rig constrained model and the free flight model are carried out. Construction of the control augmentation system for yaw and roll motion is accomplished according to the scale modified criteria of flying qualities. Effectiveness of the designed control law is demonstrated with steady pilot-in-the-loop flights at different airspeeds and angles of attack. The achieved closed-loop flying qualities are evaluated by applying multistep maneuvers for low order equivalent system identification. Whereas severe instability is observed in yaw for the open-loop case, the closed-loop flying quality of the Dutch-roll mode can be improved to level 1 at low angle of attack.

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Like albatross, unmanned aerial vehicles can significantly make use of wind gradient to extract energy by the flight technique named dynamic soaring. The research aims to develop a general optimization method to compute all the possible patterns of dynamic soaring with a small unmanned aerial vehicle. A direct collocation approach based on the Runge-Kutta integrator is proposed to solve the trajectory optimization problem for dynamic soaring. The optimal dynamic soaring trajectories are classified into two patterns: closed trajectory pattern and travelling trajectory pattern by applying terminal constraints of zero horizontal displacement and a certain travelling direction, respectively. Using different terminal constrains for heading angle and initial guesses in the optimization process, the former pattern can be divided into two subtypes: O-shaped and 8-shaped trajectories, while the latter one is divided into C-shaped, α-shaped, S-shaped and Ω-shaped trajectories. The characteristics of these patterns and the correlation among patterns are analyzed and discussed.

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Albatrosses can make use of the dynamic soaring technique extracting energy from the wind field to achieve large-scale movement without a flap, which stimulates interest in effortless flight with small unmanned aerial vehicles (UAVs). However, mechanisms of energy harvesting in terms of the energy transfer from the wind to the flyer (albatross or UAV) are still indeterminate and controversial when using different reference frames in previous studies. In this paper, the classical four-phase Rayleigh cycle, includes sequentially upwind climb, downwind turn, downwind dive and upwind turn, is introduced in analyses of energy gain with the albatross's equation of motions and the simulated trajectory in dynamic soaring. Analytical and numerical results indicate that the energy gain in the air-relative frame mostly originates from large wind gradients at lower part of the climb and dive, while the energy gain in the inertial frame comes from the lift vector inclined to the wind speed direction during the climb, dive and downwind turn at higher altitude. These two energy-gain mechanisms are not equivalent in terms of energy sources and reference frames but have to be simultaneously satisfied in terms of the energy-neutral dynamic soaring cycle. For each reference frame, energy-loss phases are necessary to connect energy-gain ones. Based on these four essential phases in dynamic soaring and the albatrosses' flight trajectory, different dynamic soaring patterns are schematically depicted and corresponding optimal trajectories are computed. The optimal dynamic soaring trajectories are classified into two closed patterns including 'O' shape and '8' shape, and four travelling patterns including 'Ω' shape, 'α' shape, 'C' shape and 'S' shape. The correlation among these patterns are analysed and discussed. The completeness of the classification for different patterns is confirmed by listing and summarising dynamic soaring trajectories shown in studies over the past decades.