Similarity-based non-singleton fuzzy logic control for improved performance in UAVs
Fu, Changhong; Sarabakha, Andriy; Kayacan, Erdal; Wagner, Christian; John, Robert; Garibaldi, Jonathan M.
CHRISTIAN WAGNER Christian.Wagner@nottingham.ac.uk
Professor of Computer Science
Robert John firstname.lastname@example.org
JONATHAN GARIBALDI JON.GARIBALDI@NOTTINGHAM.AC.UK
Professor of Computer Science
© 2017 IEEE. As non-singleton fuzzy logic controllers (NSFLCs) are capable of capturing input uncertainties, they have been effectively used to control and navigate unmanned aerial vehicles (UAVs) recently. To further enhance the capability to handle the input uncertainty for the UAV applications, a novel NSFLC with the recently introduced similarity-based inference engine, i.e., Sim-NSFLC, is developed. In this paper, a comparative study in a 3D trajectory tracking application has been carried out using the aforementioned Sim-NSFLC and the NSFLCs with the standard as well as centroid composition-based inference engines, i.e., Sta-NSFLC and Cen-NSFLC. All the NSFLCs are developed within the robot operating system (ROS) using the C++ programming language. Extensive ROS Gazebo simulation-based experiments show that the Sim-NSFLCs can achieve better control performance for the UAVs in comparison with the Sta-NSFLCs and Cen-NSFLCs under different input noise levels.
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (2017). Similarity-based non-singleton fuzzy logic control for improved performance in UAVs. https://doi.org/10.1109/FUZZ-IEEE.2017.8015440
|Conference Name||2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)|
|Start Date||Jul 9, 2017|
|End Date||Jul 12, 2017|
|Acceptance Date||Mar 14, 2017|
|Online Publication Date||Aug 23, 2017|
|Publication Date||Aug 23, 2017|
|Deposit Date||Apr 26, 2017|
|Publicly Available Date||Aug 23, 2017|
|Journal||Proceedings of the IEEE International Fuzzy Systems Conference|
|Peer Reviewed||Peer Reviewed|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf|
|Additional Information||© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Published in: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), doi: https://doi.org/10.1109/FUZZ-IEEE.2017.8015440.
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
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