Reinforcement Learning of Morphing Airfoils with Aerodynamic and Structural Effects
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This paper applies a reinforcement learning methodology to the problem of airfoil morphing. Reinforcement learning, as it is applied to morphing, is integrated with a computational model of an airfoil. The computational model uses a doublet panel method, the end yield of which is airfoil lift, drag, and moment coefficients. An episodic unsupervised learning simulation using the Q-learning method is developed to learn the optimal shape and shape change policy. Optimality is addressed by reward functions based on airfoil properties such as lift coefficient, drag coefficient, and moment coefficient about the leading edge representing optimal shapes for specified flight conditions. The methodology is demonstrated with numerical examples of a NACA type airfoil that autonomously morphs in two degrees of freedom, thickness and camber, to a shape that corresponds to specified goal requirements. Given the nature of the problem and the possibility of there being many shapes that satisfy the lift, drag, or moment coefficient requirements, the results presented in this paper show that this methodology is capable of learning the range of acceptable shapes for a given set of requirementes and morphing into one.