Model-Based and Data-Driven Covariance Control: Theory and Applications

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.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Aerospace Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Pilipovsky, Joshua Y.

Advisor dc:contributor.advisor

• Tsiotras, Panagiotis

Committee members dc:contributor.committeemember

• Chen, Yongxin
• Rogers, Jonathan
• Dörfler, Florian
• Bakolas, Efstathios

• stochastic optimal control
• data-driven control
• distributionally-robust optimization
• uncertainty quantification

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1853/77818

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/77818