Attitude Estimation of a Quadcopter with one fully damaged rotor using on-board MARG Sensors

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

392 Downloads (Pure)

Abstract

Quadcopters are becoming increasingly popular across diverse sectors. Since rotor damages occur frequently, it is essential to improve the attitude estimation and thus ultimately the ability to control a damaged quadcopter. This research is based on a state-of-the-art method that makes it possible to control the quadcopter despite the total failure of a single rotor, where the attitude and position of the quadcopter are provided by an external system. In the present research, a novel attitude estimator called Adaptive Fuzzy Complementary Kalman Filter (AFCKF) has been developed and validated that works independently of any external systems. It is able to estimate the attitude of a quadcopter with one fully damaged rotor while only relying on the on-board MARG (Magnetometer, Accelerometer, Rate Gyroscope) sensors. The AFCKF provides significantly better attitude estimates for flights with a damaged rotor than mainstream filters, estimating the roll and pitch of the quadcopter with an RMS error of less than 1.7 degrees and a variance of less than 2 degrees. The proposed filter also provides accurate yaw estimates despite the fast spinning motion of the damaged quadcopter, and thus outperforms existing methods at the cost of only a small increase in computation.
Original languageEnglish
Title of host publicationAIAA SCITECH 2022 Forum
Number of pages8
ISBN (Electronic)978-1-62410-631-6
DOIs
Publication statusPublished - 2022
EventAIAA SCITECH 2022 Forum - virtual event
Duration: 3 Jan 20227 Jan 2022

Publication series

NameAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

Conference

ConferenceAIAA SCITECH 2022 Forum
Period3/01/227/01/22

Fingerprint

Dive into the research topics of 'Attitude Estimation of a Quadcopter with one fully damaged rotor using on-board MARG Sensors'. Together they form a unique fingerprint.

Cite this