Ph.D. Defense
Joshua Pilipovsky
(Advisor: Prof. Panagiotis Tsiotras)
Model-Based and Data-Driven Covariance Control: Theory and Applications
Wednesday, January 15
12:00 P.M. EST
Location: Zoom Meeting
Meeting ID: 996 2615 4836
Passcode: 318988
Abstract
Control under uncertainty is at the cornerstone of modern control theory, yet traditional methods often struggle with ensuring safety and robustness in stochastic environments. This dissertation advances the theory of covariance steering (CS), which shifts the focus from controlling specific system states to steering entire state distributions under constraints. Historically, CS has been applied to systems with Gaussian uncertainties and boundary conditions, but real-world applications often involve non-Gaussian disturbances and ambiguous uncertainties that demand novel approaches.
This thesis contributes in two significant ways. First, it extends model-based CS theory to address complex scenarios such as non-Gaussian disturbances, risk-sensitive planning, and distributional robustness. Second, it integrates data-driven methodologies, leveraging offline system data to formulate and solve distribution steering problems for systems with unknown dynamics. The versatility of these advancements is demonstrated through applications such as spacecraft guidance, quadrotor path planning, and motion planning for autonomous vehicles, highlighting their broader impact. By uniting the precision of model-based methods with the adaptability of data-driven techniques, this research establishes a robust framework for controlling dynamical systems under uncertainty.
Committee
• Dr. Panagiotis Tsiotras – School of Aerospace Engineering, Georgia Tech (advisor)
• Dr. Yongxin Chen – School of Aerospace Engineering, Georgia Tech
• Dr. Jonathan Rogers – School of Aerospace Engineering, Georgia Tech
• Dr. Florian Dörfler – Department of Information Technology and Electrical Engineering, ETH Zurich
• Dr. Efstathios Bakolas – Department of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin