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.
Robert John email@example.com
Jonathan M. Garibaldi
© 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|
|Peer Reviewed||Peer Reviewed|
|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|
|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.
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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