Frequency Domain Identification and Model-based Disturbance Observer for a Mini Drone

Abstract

Drone is an innovative industry that can combine the application of various technologies in the fourth industrial era, such as big data, artificial intelligence, and ICT. Although the synergy effects of these technologies will be great in various industrial ecosystems, drones are vulnerable to gusts such as 'building wind' or 'valley wind'. Herein, the frequency domain of a mini drone was identified and a model-based disturbance observer (DOBs) was applied to implement the drone robust resistance against gusts. The frequency response of the Parrot Mambo or mini drone was measured with multi-sine excitation and the system dynamic parameters were identified. Based on the identified model, DOBs were designed and applied to the drone’s altitude, position, and yaw control. The effectiveness of the DOBs was verified with a sinusoidal disturbance. With the model-based DOB, 84.5% of the drone altitude responses, 50.7% of x responses, 52.1% of y responses, and 79.7% of yaw responses against sinusoidal disturbances were reduced. Flight responses were measured against wind disturbances with changing speed and direction. With the model-based DOBs, the drone's altitude decreased by 87.7%, the x position by 53.0%, the y position by 60.6%, and the yaw angle by 56.2%.