Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning
This addresses safety and reliability issues for quadcopter operations in applications like delivery or inspection, though it is incremental as it builds on existing fault detection and control methods.
The paper tackled the problem of mid-flight propeller failures in quadcopters by developing a reinforcement learning-based adaptive controller and a neural network detection system, achieving stable flight and efficient waypoint tracking for quadcopters with 4, 3, or 2 functional propellers, with the detection system quickly identifying failures.
Quadcopters can suffer from loss of propellers in mid-flight, thus requiring a need to have a system that detects single and multiple propeller failures and an adaptive controller that stabilizes the propeller-deficient quadcopter. This paper presents reinforcement learning based controllers for quadcopters with 4, 3, and 2 (opposing) functional propellers. The paper also proposes a neural network based propeller fault detection system to detect propeller loss and switch to the appropriate controller. The simulation results demonstrate a stable quadcopter with efficient waypoint tracking for all controllers. The detection system is able to detect propeller failure in a short time and stabilize the quadcopter.