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Methods for Robust Learning-Based Control


O'Connell, Michael Thomas (2023) Methods for Robust Learning-Based Control. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/2xnc-t162.


This thesis addresses the general problem of improving control, safety, and reliability of multi-rotor drones in various challenging conditions by introducing novel deep-learning-based approaches. These approaches are designed to tackle specific issues that multi-rotor drones face during operation, such as near-ground trajectory control, high-speed wind disturbances, actuation delays, and motor failures. The thesis is organized into four main chapters, plus an introduction and conclusion. Each of the main chapters focuses on a unique approach to address a particular challenge of deep-learning-based control methods. Chapter 2 presents Neural-Lander, a deep-learning-based robust nonlinear controller that significantly improves quadrotor control performance during landing by accounting for complex aerodynamic effects. This chapter addresses key challenges to incorporating learned residual dynamics into a control architecture, laying the groundwork for the subsequent chapters. Chapters 3 and 4 introduce Neural-Fly, a learning-based approach that uses Domain Adversarially Invariant Meta-Learning (DAIML) and adaptive control to enable rapid online learning and precise flight control under a wide range of wind conditions. Chapter 5 proposes a lightweight augmentation method that enhances trajectory tracking performance for UAVs by effectively compensating for motor dynamics and digital transport delays. This method is extensible to a range of control methods, including learning-based approaches. Chapter 6 explores a novel sparse failure identification method for detecting and compensating for motor failures in over-actuated UAVs, contributing to the development of robust fault detection and compensation strategies for a safer and more reliable operation. This method builds on the Neural-Fly online learning framework and extends it to handle a wider range of conditions, including complete actuator failures. Together, these chapters address key challenges in safe and reliable learning-based control and demonstrate the potential of deep-learning-based control methods.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:control; machine learning; nonlinear control; adaptive control; flight control
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Space Engineering
Awards:Ernest E. Sechler Memorial Award in Aeronautics, 2020.
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Chung, Soon-Jo
Thesis Committee:
  • Yue, Yisong (chair)
  • Burdick, Joel Wakeman
  • Pellegrino, Sergio
  • Chung, Soon-Jo
Defense Date:3 May 2023
Record Number:CaltechTHESIS:06072023-134620248
Persistent URL:
Related URLs:
URLURL TypeDescription 3 and 4 original publication 2 original publication
O'Connell, Michael Thomas0000-0001-6681-8823
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:16091
Deposited By: Michael O'Connell
Deposited On:08 Jun 2023 17:06
Last Modified:08 Nov 2023 18:38

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