Similarity-based non-singleton fuzzy logic control for improved performance in UAVs
Document Type
Conference Proceeding
Publication Date
8-23-2017
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.
Publication Title
IEEE International Conference on Fuzzy Systems
Recommended Citation
Fu, C.,
Sarabakha, A.,
Kayacan, E.,
Wagner, C.,
John, R.,
&
Garibaldi, J.
(2017).
Similarity-based non-singleton fuzzy logic control for improved performance in UAVs.
IEEE International Conference on Fuzzy Systems.
http://doi.org/10.1109/FUZZ-IEEE.2017.8015440
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10485