A Data-Driven Approach for Control and Stabilization of a Single Actuator Monocopter

Abstract

In this paper, we use a machine learning approach to stabilize a Single Actuator Monocopter (SAM), showing its ability to operate autonomously outdoors utilizing an onboard Inertial Measurement Unit (IMU). We introduce a neural network-based proportional stabilizer that works in parallel to cascaded P/PID controllers. This network uses the IMU’s data to predict the world frame angular velocity, which is then used to stabilize the SAM. Training data was collected to establish correspondences between the IMU readings and the world frame angular velocity from flights conducted within an indoor motion capture environment. We used data augmentation to improve the network’s generalization and prediction performance by 9%. Once trained, the neural network was deployed on the SAM to estimate its angular velocity in real time. We then tested the SAM’s autonomous capabilities in a large semi-outdoor space of approximately 16,000 m3 with wind disturbances of up to 1.5 m/s. We demonstrate position hold, waypoint, and continuous tracking tests, achieving median position errors of 0.5 m, 1.05 m, and 2.22 m, respectively, where no stabilization would result in failure of the defined tests.