Date of Award
2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical Engineering
Committee Chair
Farbod Fahimi
Committee Member
Avimanyu Sahoo
Committee Member
Rohan Sood
Committee Member
Howard Chen
Committee Member
Chang-kwon Kang
Research Advisor
Farbod Fahimi
Subject(s)
Adaptive control systems, Self-organizing systems, Neural networks (Computer science), Machine learning
Abstract
Aerospace and robotic systems perform various tasks in uncertain, dynamic environments. Further, the data that a system encounters in the real-world is often the most valuable. The most advanced aerospace and robotic systems of the future will be able to learn online, during operation, from this real-world data. Aerospace and robotic systems are often expensive and difficult to model, and the controllers of these systems thus require rigorous control proofs and safety guarantees. Recently, general research in artificial intelligence and machine learning (AI/ML) has made significant strides in developing learning-based systems and controllers. However, much of this research has focused on optimizing control performance, robustness, or prediction accuracy, without considering the stability and safety requirements for control of real-world aerospace and robotic systems. Additionally, the optimization objectives of adaptive control and machine learning, adapting parameters over time to achieve some desired goal or performance, are often closely related. Thus, the theory and mathematical rigor of adaptive control can be used to augment popular AI/ML tools for stability guarantees and online learning. This dissertation discusses research progress in utilizing AI/ML tools, namely deep neural networks, together with adaptive control to achieve provably-stable online learning and optimization. Part I first describes “learning for control,” where neural networks are stably used in a fully online model-based nonlinear controller. The derived controller is shown to desirably control robotic arms, spacecraft, and quadcopters under various disturbances and model uncertainties with limited a priori modeling. Next, Part II describes “control for learning,” where control-theoretic techniques are used to stably update deep neural network parameters online. The proposed update law is shown to give desirable performance when deep neural network outputs are used in predicting or controlling dynamical systems, especially under domain shift from the training distribution to the target distribution, common in forecasting and sim-to-real transfer of control policies. Throughout this dissertation, the connections between machine learning and adaptive control are explored, with each field acutely poised to benefit the other.
Recommended Citation
Elkins, Jacob G., "Online learning for adaptive control : stable learning and control for aerospace and robotics" (2024). Dissertations. 414.
https://louis.uah.edu/uah-dissertations/414