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.
Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning
A neural network-based fault detection system and reinforcement learning controllers enable stable quadcopter operation with varying numbers of functional propellers.
- Year
- 2020
- Venue
- arXiv 2020
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- 3
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2002.11564v2ARXIV-DEFAULT
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