Inspired by nature, smart morphing technologies enable the aircraft of tomorrow to sense their environment and adapt the shape of their wings in flight to minimize fuel consumption and emissions. A primary challenge on the road to this feature is how to use the knowledge gathered from sensory data to establish an optimal shape adaptively and continuously in flight. To address this challenge, this article proposes an online black-box aerodynamic performance optimization architecture for active morphing wings. The proposed method integrates a global online-learned radial basis function neural network (RBFNN) model with an evolutionary optimization strategy, which can find global optima without requiring in-flight local model excitation maneuvers. The actual wing shape is sensed via a computer vision system, while the optimized wing shape is realized via nonlinear adaptive control. The effectiveness of the optimization architecture was experimentally validated on an active trailing-edge (TE) camber morphing wing demonstrator with distributed sensing and control in an open jet wind tunnel. Compared with the unmorphed shape, a <inline-formula> <tex-math notation="LaTeX">$7.8\%$</tex-math> </inline-formula> drag reduction was realized, while achieving the required amount of lift. Further data-driven predictions have indicated that up to <inline-formula> <tex-math notation="LaTeX">$19.8\%$</tex-math> </inline-formula> of drag reduction is achievable and have provided insight into the trends in optimal wing shapes for a wide range of lift targets.

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Recent trends in aviation highlight the ever-increasing need for fuel economy and sustainability. Active morphing technology can offer significant benefits over conventional wing designs. Inspired by nature, smart morphing technologies enable the aircraft of tomorrow to sense their environment and adapt the shape of their wings in-flight to minimize fuel consumption and emissions. A primary challenge on the road to this future is the question of how to use the knowledge gathered from sensory data to establish an optimal shape
adaptively and continuously in-flight.

To address this challenge, this thesis proposes a novel architecture for online black-box aerodynamic performance optimization for active morphing wings. The proposed method seeks to extend the scope of state-of-the-art online performance optimization methods by integrating a global online-learned radial basis function neural network model with a derivative-free evolutionary optimization strategy. The effectiveness of the optimization strategy was tested on a Vortex Lattice Method aerodynamic model of an over-actuated morphing wing that was corrected using previously collected wind tunnel data. Simulations show that the proposed method is able to control the morphing shape and angle of attack to achieve various target lift coefficients with better aerodynamic efficiency than the unmorphed wing shape. Furthermore, the effectiveness of the optimization architecture was experimentally evaluated on an active trailing-edge camber morphing wing demonstrator with distributed sensing and control, the SmartX-Alpha, in the open jet facility of Delft University of Technology. Compared to the unmorphed shape, a 7.8 % drag reduction was realized, while achieving the required amount of lift. Further data-driven predictions have indicated that even higher reductions in drag are achievable and have provided insight into the trends in optimal wing shapes for a wide range of lift targets.