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

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