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1. Comparison of Traditional Optimal Control Methodologies to Reinforcement Learning Agents in Quadcopter Attitude Control Applications

Description:: Attitude control in quadrotor Unmanned Aerial Vehicle (UAV) systems is traditionally managed by optimal control loops tuned to minimize errors in performance. While robust, these loops perform sub-optimally in dynamic and unpredictable environments which inspire new interest in sophisticated solution and approaches such as reinforcement learning (RL) approaches which should...
Keyword:: Intelligent Systems, Reinforcement Learning, Quadcopters, Learning and Adaptive Systemsons, Aerial Systems: Mechanics and Control, Proximal Policy Optimization, Drones, and Attitude Control
Subject:: Space vehicles--Attitude control systems, Proximal Policy Optimization, Reinforcement learning, Policy Gradient, Drone aircraft, and Machine learning
Creator:: Stephen Kleppinger,
Contributor:: Bharadwaj, Shreenidhi (advisor)
Owner:: Stephen Michael Kleppinger
Publisher:: Northwestern University
Language:: English
Date Uploaded:: 04/05/2022
Date Modified:: 04/05/2022
Date Created:: 12-2021
Resource Type:: Masters Thesis

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1. Comparison of Traditional Optimal Control Methodologies to Reinforcement Learning Agents in Quadcopter Attitude Control Applications