Hybrid Reinforcement Learning Control for a Micro Quadrotor Flight | IEEE Journals & Magazine | IEEE Xplore

Hybrid Reinforcement Learning Control for a Micro Quadrotor Flight


Abstract:

This letter presents a combination of reinforcement learning (RL) and deterministic controllers to learn a quadrotor control. Learning the quadrotor flight in a standard ...Show More

Abstract:

This letter presents a combination of reinforcement learning (RL) and deterministic controllers to learn a quadrotor control. Learning the quadrotor flight in a standard RL approach requires many iterations of trial and error, which may bring about risky exploration and battery consumption. In this letter, we integrate a classical controller such as PD (proportional and derivative) or LQR (linear quadratic regulator) with a RL policy using their linear combination. The proposed method is not only simple to use, but also fast in learning convergence. When the algorithm is evaluated for a quadrotor trajectory tracking by means of a velocity control for both simulation and experiment, it demonstrates the faster convergence rate and better control performance in comparison with an existing rapid model-based RL method.
Published in: IEEE Control Systems Letters ( Volume: 5, Issue: 2, April 2021)
Page(s): 505 - 510
Date of Publication: 11 June 2020
Electronic ISSN: 2475-1456

Funding Agency:


References

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