What is it about?
First, the aerodynamic data of the missile is collected through forced pitching motion. And then, an aerodynamic model is constructed using a deep neural network. Afterward, the fidelity of the model are checked with the open-loop control law. Finally, a missile pitch control law is generated through deep reinforcement learning based on the aerodynamic model.
Photo by Kurt Cotoaga on Unsplash
Why is it important?
This study verifies the possibility of applying a deep neural network to air-vehicle aerodynamic identification and deep reinforcement learning to a complicated flight control law design with excellent generalization ability. Machine learning is expected to play a significant role in the design and research for the novel generation of air-vehicle.
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This page is a summary of: Aerodynamic Identification and Control Law Design of a Missile Using Machine Learning, AIAA Journal, April 2023, American Institute of Aeronautics and Astronautics (AIAA),
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