Date of Award
Summer 7-2021
Access Type
Thesis - Open Access
Degree Name
Master of Science in Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Hever Moncayo
First Committee Member
Richard Prazenica
Second Committee Member
Kadriye Merve Dogan
Abstract
With the growing use of Unmanned Aerial Systems, a new need has risen for intelligent algorithms that not only stabilize or control the system, but rather would also include various factors such as optimality, robustness, adaptability, tracking, decision making, and many more. In this thesis, a deep-learning-based control system is designed with fault-tolerant and disturbance rejection capabilities and applied to a high-order nonlinear dynamic system. The approach uses a Reinforcement Learning architecture that combines concepts from optimal control, robust control, and game theory to create an optimally adaptive control for disturbance rejection. Additionally, a cascaded Observer-based Kalman Filter is formulated for estimating adverse inputs to the system. Numerical simulations are presented using different nonlinear model dynamics and scenarios. The Deep Reinforcement Learning and Observer architecture is demonstrated to be a promising control system alternative for fault tolerant applications.
Scholarly Commons Citation
Aoun, Christoph Elias, "Fault Tolerant Deep Reinforcement Learning for Aerospace Applications" (2021). Doctoral Dissertations and Master's Theses. 603.
https://commons.erau.edu/edt/603
