Fuzzy Logic Based Approach to Design of Autonomous Landing System for Unmanned Aerial Vehicles

Çetin, Ömer; Kurnaz, Sefer; Kaynak, Okyay

doi:10.1007/s10846-010-9508-6

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…Even today, most UAV landings are performed manually by their operators from inside the drone’s ground station, depriving the drone pilot of peripheral vision, instinctive senses, and space perception during the procedure. As a result, the majority of UAV’s crashes happen due to human errors during takeoffs and landings, which constitute the most dangerous parts of a drone’s flight [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing

Tsitses,

Zacharia,

Xidias

et al. 2024

Sensors

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This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs: the range from the deck, the relative wind direction and speed, the airspeed, and the UAV’s flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship’s velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

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Section: Introductionmentioning

confidence: 99%

“…In [ 4 ], a control system for autonomous flight and landing based on fuzzy logic is introduced. The system consists of three fuzzy logic controllers for autonomous navigation in UAVs, and three additional fuzzy logic modules are created within the primary landing system.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing

Tsitses,

Zacharia,

Xidias

et al. 2024

Sensors

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“…Today, air travel and the necessity of using these facilities are because of military needs, and the intensity of civil aviation is increasing [4]. There are publications in the literature about fuzzy logic applications in airports [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Planning of airport pavement with artificial intelligence methods

KÜÇÜKÇAPRAZ

Terzi

2021

Journal of Innovative Transportation

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“…Type-1 fuzzy logic controller (T1FLC) structures that are one of the intelligent controller structures have been successfully used in many applications. [25][26][27][28] The T1FLCs are based on human experience and knowledge because they do not need a mathematical model of the system to be controlled. However, T1FLCs may not have the ability to cope with uncertainties, disturbances, and parameter variations in the system.…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of type‐2 neuro‐fuzzy controller‐based direct power control for three‐phase active front‐end rectifiers

Açıkgöz

Kumar²,

Beiranvand

et al. 2019

Int Trans Electr Energ Syst

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Summary Simple implementation, robust response, good dynamic, and steady‐state performances are empowering direct power control (DPC) as an accepted control method in power electronic converters. One of the key converters are active front‐end rectifiers that are used in many applications. In this paper, type‐2 neuro‐fuzzy controller based DPC (T2NFC‐DPC) is designed for active front‐end rectifier. T2NFC uses an upper and lower bounds for membership functions and provides a better tool for defining uncertain systems and also neural networks help to improve the speed of convergence in tracking the reference signals. In the proposed method, error and change of error are used to train T2NFC‐DPC. The effectiveness of the proposed method is validated using experimental model. An experimental setup is developed using dSPACE 1104 control board in order to show the applicability of the proposed method. Experimental results show that T2NFC‐DPC outperforms against conventional proportional‐integral DPC (PI‐DPC) method.

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Fuzzy Logic Based Approach to Design of Autonomous Landing System for Unmanned Aerial Vehicles

Cited by 20 publications

References 9 publications

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing

Planning of airport pavement with artificial intelligence methods

Hardware implementation of type‐2 neuro‐fuzzy controller‐based direct power control for three‐phase active front‐end rectifiers

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