Learning in dynamical systems with guarantees: from system identification to safety verification and fast adaptation

This thesis investigates the sample complexity of data-driven algorithms for learning in controlled dynamical systems. As more systems operate in environments where models are unknown but data are available, understanding how efficiently learning algorithms use data becomes increasingly important. The thesis addresses three central problems in this context. First, it establishes conditions under which unknown parameters in nonlinear systems can be efficiently learned via non-active exploration, showing that linearly parameterized systems with real-analytic features can be identified using least-squares and set-membership estimators. Second, it develops a framework for verifying safety and synthesizing parameters in unknown systems with hybrid state spaces, leveraging a new bandit-based method—Hybrid Hierarchical Optimistic Optimization (HyHOO)—that extends prior work in black-box optimization. Finally, the thesis explores meta-learning for optimal control, proposing methods to exploit shared structure across related control tasks for fast adaptation in new control tasks. The results contribute theoretical guarantees, practical algorithms, and experimental validations toward a deeper understanding of data efficiency in learning dynamical systems.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Musavi, Negin

Contributors dc:contributor

• Dullerud, Geir E.
• Dullerud, Geir
• Li, Yingying
• Mitra, Sayan
• Srikant, Rayadurgam
• West, Matthew

• System Identification
• Safety Verification
• Meta Learning

Statement dc:rights

• Copyright 2025 Negin Musavi

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129869

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129869