Colloquium: Chris Yoon Sang Chung (C&O)

Loss-of-Control (LoC) is the primary cause of drone crashes, necessitating efficient onboard prevention systems. This study introduces a data-driven approach for detecting LoC in quadrotors, using Critical Slowing Down (CSD) theory as an Early-Warning-Signal (EWS) of approaching a critical-transition. This paper employs a Fuzzy-Logic Inference System (FLIS) to aggregate the CSD metrics alongside other EWS indicators, such as actuator phase delay. The proposed FLIS is applied to two LoC modes: a yaw-induced LoC and a roll-induced LoC. This approach proposes novel EWS indicators and a LoC detector and is generalizable across varying mass/size without needing precise state estimation of the quadrotor, instead only relying on onboard gyro and rotor speed data. Using real flight data from a GEPRO quadrotor, and a custom-built drone mounted on a gimbal testing rig, this paper demonstrates that various EWS indicators inferred with FLIS can provide accurate, and timely detection of an upcoming LoC event.

Supervisor: Dr.ir. Coen de Visser