Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A Comparative study of learning based control policies and conventional controllers on 2D bi-rotor platform with tail assistance
Download
index.pdf
Date
2019
Author
Uğurlu, Halil İIbrahim
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
167
views
81
downloads
Cite This
With the developing technology, multi-rotor platforms have become widespread and their control has become an important problem. In this thesis, we analyze physical extensions and control approaches for better control of rotor platforms. The first main contribution of the thesis is whether a tail-appendage that is attached under a multi-rotor platform can improve the multi-rotor's performance. Moreover, we used conventional control approaches as well as Deep Reinforcement Learning to learn a policy for controlling rotor platforms with or without tail appendage. To obtain better training and testing performance with Deep Reinforcement Learning, we adopted a curricular learning approach, where the difficulty of training samples is gradually increased. For the experiments, a two-dimensional simulation environment is developed to simulate a bi-rotor flying system, the counterpart of quad-rotors in three-dimensions. Both control strategies are rigorously analyzed for controlling the platform with and without tail appendage in this simulation environment.
Subject Keywords
Drone aircraft.
,
Keywords: Deep Reinforcement Learning
,
multi-rotor UAVs
,
Artificial Neural Networks.
URI
http://etd.lib.metu.edu.tr/upload/12624062/index.pdf
https://hdl.handle.net/11511/44195
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
A Study on the Efficacy of Deep Reinforcement Learning for Intrusion Detection
GÜLMEZ, HALİM GÖRKEM; Angın, Pelin (2021-04-01)
The world has witnessed a fast-paced digital transformation in the past decade, giving rise to all-connected environments. While the increasingly widespread availability of networks has benefited many aspects of our lives, providing the necessary infrastructure for smart autonomous systems, it has also created a large cyber attack surface. This has made real-time network intrusion detection a significant component of any computerized system. With the advances in computer hardware architectures with fast, hi...
An Effective Forest Fire Detection Framework Using Heterogeneous Wireless Multimedia Sensor Networks
Kizilkaya, Burak; Ever, Enver; Yatbaz, Hakan Yekta; Yazıcı, Adnan (2022-05-01)
With improvements in the area of Internet of Things (IoT), surveillance systems have recently become more accessible. At the same time, optimizing the energy requirements of smart sensors, especially for data transmission, has always been very important and the energy efficiency of IoT systems has been the subject of numerous studies. For environmental monitoring scenarios, it is possible to extract more accurate information using smart multimedia sensors. However, multimedia data transmission is an expensi...
A SYSTEMATIC REVIEW ON SMART CITY SERVICES AND IOT-BASED TECHNOLOGIES
Özkan Yıldırım, Sevgi (2019-03-21)
Due to the technological developments, Internet of things (IoT) has become a real phenomenon. Accordingly, many IoT-based smart concepts appeared in our daily lives such as smart home, smart healthcare and smart city. There are several factors accelerating or hindering the adoption of such new services and concepts. So, the acceptance of IoT-based smart services is critical and should be analyzed carefully. In this study, we aimed to prepare a proper starting point for future studies on end user acceptance ...
A simulation environment for cybersecurity attack analysis based on network traffic logs
Daneshgadeh, Salva; Oney, Mehmet Ugur; Kemmerich, Thomas; Baykal, Nazife (2019-01-01)
The continued and rapid progress of network technology has revolutionized all modern critical infrastructures and business models. Technologies today are firmly relying on network and communication facilities which in turn make them dependent on network security. Network-security investments do not always guarantee the security of organizations. However, the evaluation of security solutions requires designing, testing and developing sophisticated security tools which are often very expensive. Simulation and...
A systematic approach for attack analysis and mitigation in V2V networks
Bhargava, Bharat; Johnson, Amber; Munyengabe, Gisele; Angın, Pelin (2016-03-01)
The increasing popularity of V2V networks with the rise of driverless cars in the recent years have made them vulnerable to attacks in growing complexity. Accurate assessment of the safety and security needs of V2V networks based on context and the costs associated with attack mitigation mechanisms are significant for successful operation of these networks despite adversaries trying to disrupt their functions. In this paper we provide an analysis of the major security and reliability issues in V2V networks,...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
H. İ. Uğurlu, “A Comparative study of learning based control policies and conventional controllers on 2D bi-rotor platform with tail assistance,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2019.