TY - GEN
T1 - Parity-based diagnosis in uavs
T2 - 2019 International Conference on Robotics and Automation, ICRA 2019
AU - Zogopoulos-Papaliakos, Georgios
AU - Kyriakopoulos, Kostas J.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Parity-Based methodologies for fault diagnosis in UAVs often result in nonlinear residual generators. Still, a systematic framework to perform detectability and robustness analyses of residual generators does not exist. In this work, detectability and robustness metrics for static and dynamic residuals are presented, while numerical methods, specifically Particle Swarm Optimization, are employed to calculate them. The results are used to characterize the performance of a fault detection system. An application on a UAV model is shown, based on real flight data.
AB - Parity-Based methodologies for fault diagnosis in UAVs often result in nonlinear residual generators. Still, a systematic framework to perform detectability and robustness analyses of residual generators does not exist. In this work, detectability and robustness metrics for static and dynamic residuals are presented, while numerical methods, specifically Particle Swarm Optimization, are employed to calculate them. The results are used to characterize the performance of a fault detection system. An application on a UAV model is shown, based on real flight data.
UR - http://www.scopus.com/inward/record.url?scp=85069457404&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069457404&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2019.8794125
DO - 10.1109/ICRA.2019.8794125
M3 - Conference contribution
AN - SCOPUS:85069457404
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 6496
EP - 6502
BT - 2019 International Conference on Robotics and Automation, ICRA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 May 2019 through 24 May 2019
ER -