Keeping a drone’s flight control system on the right track with Meta Learning
A new, machine learning-based adaptive control algorithm can keep drones on target – MIT Engineering
MIT researchers developed a new adaptive control system that could help autonomous drones stay on target in uncertain environments. The system automatically learns to adapt to unknown disturbances such as gusting winds. The researchers use a technique called Meta Learning that can train the system. With Meta learning it is possible to determine the optimization algorithm and the geometry of specific disturbances the drone is facing.
Unlike standard approaches, the new technique does not require the person programming the autonomous drone to know anything in advance about the structure of these uncertain disturbances. Instead, the control system’s artificial intelligence model learns all it needs to know from a small amount of observational data collected from 15 minutes of flight time.
“The concurrent learning of these components is what gives our method its strength. By leveraging meta-learning, our controller can automatically make choices that will be best for quick adaptation,” says Navid Azizan, who is the Esther and Harold E. Edgerton Assistant Professor in the MIT Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS).
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