VladGavra98/SERL
Safety-informed Evolutionary Reinforcement Learning applied to fault-tolerant and robust flight control.
This project helps aerospace engineers and flight control system designers develop intelligent fault-tolerant control systems for fixed-wing aircraft. It takes high-fidelity aircraft models and simulated fault conditions as input, and outputs robust control policies that maintain stable flight even with external disturbances or subsystem failures. The end-user is typically an aerospace engineer focused on flight dynamics and control.
No commits in the last 6 months.
Use this if you are an aerospace engineer looking to design and test advanced control algorithms that can automatically compensate for various aircraft faults and challenging flight conditions.
Not ideal if you are working with aircraft other than fixed-wing models, or if you need to deploy controllers on hardware that cannot run Linux-based Python environments.
Stars
10
Forks
2
Language
Python
License
MIT
Category
Last pushed
Jul 18, 2024
Commits (30d)
0
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