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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.

Authors

Changhong Fu

Andriy Sarabakha

Erdal Kayacan

Christian Wagner

Robert John robert.john@nottingham.ac.uk

Jonathan M. Garibaldi



Abstract

© 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.

Start Date Jul 9, 2017
Publication Date Aug 23, 2017
Journal Proceedings of the IEEE International Fuzzy Systems Conference
Electronic ISSN 1544-5615
Peer Reviewed Peer Reviewed
ISBN 9781509060351
APA6 Citation 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
DOI https://doi.org/10.1109/FUZZ-IEEE.2017.8015440
Publisher URL http://ieeexplore.ieee.org/document/8015440/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../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.

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Copyright Statement
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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