Aerodynamic modeling, identification, and control for a missile using machine learning.

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.

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Photo by Kurt Cotoaga on Unsplash

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.

Perspectives