Similarity-based non-singleton fuzzy logic control for improved performance in UAVs
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...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE
2017
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| Online Access: | https://eprints.nottingham.ac.uk/42286/ |
| _version_ | 1848796453905891328 |
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| author | Fu, Changhong Sarabakha, Andriy Kayacan, Erdal Wagner, Christian John, Robert Garibaldi, Jonathan M. |
| author_facet | Fu, Changhong Sarabakha, Andriy Kayacan, Erdal Wagner, Christian John, Robert Garibaldi, Jonathan M. |
| author_sort | Fu, Changhong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:48:14Z |
| format | Conference or Workshop Item |
| id | nottingham-42286 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:48:14Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-422862020-05-04T19:02:09Z https://eprints.nottingham.ac.uk/42286/ 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. 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. IEEE 2017-08-23 Conference or Workshop Item PeerReviewed Fu, Changhong, Sarabakha, Andriy, Kayacan, Erdal, Wagner, Christian, John, Robert and Garibaldi, Jonathan M. (2017) Similarity-based non-singleton fuzzy logic control for improved performance in UAVs. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), 9-12 Jul 2017, Naples, Italy. http://ieeexplore.ieee.org/document/8015440/ doi:10.1109/FUZZ-IEEE.2017.8015440 doi:10.1109/FUZZ-IEEE.2017.8015440 |
| spellingShingle | Fu, Changhong Sarabakha, Andriy Kayacan, Erdal Wagner, Christian John, Robert Garibaldi, Jonathan M. Similarity-based non-singleton fuzzy logic control for improved performance in UAVs |
| title | Similarity-based non-singleton fuzzy logic control for improved performance in UAVs |
| title_full | Similarity-based non-singleton fuzzy logic control for improved performance in UAVs |
| title_fullStr | Similarity-based non-singleton fuzzy logic control for improved performance in UAVs |
| title_full_unstemmed | Similarity-based non-singleton fuzzy logic control for improved performance in UAVs |
| title_short | Similarity-based non-singleton fuzzy logic control for improved performance in UAVs |
| title_sort | similarity-based non-singleton fuzzy logic control for improved performance in uavs |
| url | https://eprints.nottingham.ac.uk/42286/ https://eprints.nottingham.ac.uk/42286/ https://eprints.nottingham.ac.uk/42286/ |