A Velocity Controller for Quadrotors Based on Reinforcement Learning

UAVs based on PID controllers are having increasing difficulties in handling complex tasks. Whereas, reinforcement learning-based high-dimensional models provide an important entry point for flight control to handle complex and high-dimensional tasks. In this paper, a neural network controller training framework for outer-loop control is proposed, which is used as a base platform for velocity controller training. Also, a reinforcement learning-based quadrotor neural network speed controller is proposed which maps the state of the UAV to the throttle commands of the rotor for stable control of speed. In addition, this paper employs the idea of curriculum learning to help the UAV adapt to a larger speed tracking range and improve its overall performance. We demonstrate the performance of the trained neural network controller by comparing it with a conventional PI controller in simulations, achieving improvements in both steady-state response time and tracking performance.