Methods for Robust Learning-Based Control

Citation

O'Connell, Michael Thomas (2023) Methods for Robust Learning-Based Control. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/2xnc-t162. https://resolver.caltech.edu/CaltechTHESIS:06072023-134620248

Abstract

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.

Thesis Files