Low-Level Controller in Response to Changes in Quadrotor Dynamics

The dynamics of all real quadrotors inevitably differ even if they are the same product. In particular, the dynamics can change significantly during the flight due to additional device attachments or overheating motors. In this study, we focus on training a low-level controller, which operates in response to dynamics-changes without prior knowledge or fine-tuning of the parameters, using reinforcement learning. We randomize the dynamics of quadrotors in the simulator and train the policy based on dynamics information extracted from the state–action history through recurrent neural networks (RNNs). In addition, our experiment demonstrates the diffi- culties in applying existing actor-critic structures that extract dynamics information using end-to-end RNNs for unstable quadrotors; hence, we propose a novel structure with bet- ter performance. Finally, the excellent performance of the proposed controller is verified by testing experiments that stabilize quadrotors with different dynamics. The experiment videos and the code can be found at https://github.com/ jackyoung96/RNN-Quadrotor-controller.