Machine learning based integrated design to aerodynamic optimization and guidance for cruise missile

DOI:	10.11809/bqzbgcxb2024.09.006
Keywords:	aerodynamic optimization; guidance law; surrogate model; integrated design; machine learning; Deep Q Network
Abstract:	A machine learning based aerodynamic optimization and guidance integration design method is proposed for the terminal guidance of the cruise missile. Taking the cruise missile’s wing as the optimization object, the method firstly establishes a fully connected neural network based aerodynamic parameter surrogate model, and combines the model with a physical parameter optimization neural network for aerodynamic optimization, thus constructing an online aerodynamic optimization model. Secondly, the guidance environment of reinforcement learning is constructed by coupling the optimization model with the flight dynamics equation set of the cruise missile, and an intelligent guidance law is designed by using the Deep Q Network reinforcement learning algorithm, thus realizing the integrated design of aerodynamic optimization and guidance. Through the simulation experiments of hitting the ground stationary target, compared with the guidance law without aerodynamic optimization, it shows that online aerodynamic optimization can make the cruise missile fly with the optimal lift to drag ratio, which shortens the guidance time and saves the fuel; the integrated model can produce more continuous angle of attack guidance commands by using only the line of sight angle rate, which improves the guidance accuracy.
Issue:	Vol. 45 No. 9 (2024)
Published:	2024-09-30
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