Date of Award
Fall 11-22-2024
Document Type
Dissertation
Degree Name
Ph.D. in Electrical Engineering
Organizational Unit
Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering
First Advisor
Kimon P. Valavanis
Second Advisor
Matthew J. Rutherford
Third Advisor
Alvaro Arias
Fourth Advisor
Haluk Ogmen
Fifth Advisor
Rui Fan
Copyright Statement / License for Reuse
All Rights Reserved.
Keywords
Adaptive control, Deep deterministic policy gradient (DDPG), Multirotor unmanned aerial vehicles (UAVs), Optimal control, Reinforcement learning
Abstract
In recent decades, unmanned systems, particularly Unmanned Aerial Vehicles (UAVs), have seen significant advancement and unprecedented growth in military, civilian and public domain applications. Scientists have focused on enhancing UAV navigation and control through cutting-edge technologies and support tools. UAVs find applications in many fields, except military, such as agriculture, infrastructure inspection, wildlife monitoring, search and rescue, emergency response, border protection, to name but a few relevant civilian applications. Given the faster-than-exponential increase of available computational power, learning-based algorithms have emerged as a prominent tool for (real-time) multirotor UAV navigation and control. This dissertation centers around the fusion of conventional control methods with learning algorithms to fine-tune and/or identify and estimate multirotor UAV aerodynamic parameters (model parameters), as well as controller parameters and gains, also tackling uncertainties. The combined multilayer navigation control architecture, based on obtained results, shows promise as it is real-time applicable and implementable.
Copyright Date
3-2025
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Serhat Sönmez
Provenance
Received from author
File Format
application/pdf
Language
English (eng)
Extent
143 pgs
File Size
8.8 MB
Recommended Citation
Sönmez, Serhat, "Model-Based Navigation and Control of Multirotor UAVs: A Machine Learning Approach" (2024). Electronic Theses and Dissertations. 2521.
https://digitalcommons.du.edu/etd/2521