Novel Levenberg-Marquardt based learning algorithm for unmanned aerial vehicles

DOI10.1016/J.INS.2017.07.020zbMath1447.93196OpenAlexW2735519414MaRDI QIDQ778470

Mojtaba Ahmadieh Khanesar, Erdal Kayacan, Nursultan Imanberdiyev, Andriy Sarabakha, Hani Hagras

Publication date: 2 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2017.07.020

zbMATH Keywords

sliding mode controlfuzzy neural networksLevenberg-Marquardt algorithmunmanned aerial vehicletype-1 fuzzy logic control

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) Adaptive control/observation systems (93C40) Automated systems (robots, etc.) in control theory (93C85) Variable structure systems (93B12) Networked control (93B70)

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Cites Work

• Universal approximation of a class of interval type-2 fuzzy neural networks in nonlinear identification
• Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction
• Neural-network-based sliding-mode control for multiple rigid-body attitude tracking with inertial information completely unknown
• Modeling uncertainty with fuzzy logic. With recent theory and applications
• Introduction to Type‐2 Fuzzy Logic Control
• Introduction to feedback control of underactuated VTOLvehicles: A review of basic control design ideas and principles

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