Learning in dynamical systems with guarantees: from system identification to safety verification and fast adaptation
Musavi, Negin
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https://hdl.handle.net/2142/129869
Description
Title
Learning in dynamical systems with guarantees: from system identification to safety verification and fast adaptation
Author(s)
Musavi, Negin
Issue Date
2025-07-16
Director of Research (if dissertation) or Advisor (if thesis)
Dullerud, Geir E.
Doctoral Committee Chair(s)
Dullerud, Geir
Committee Member(s)
Li, Yingying
Mitra, Sayan
Srikant, Rayadurgam
West, Matthew
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
System Identification
Safety Verification
Meta Learning
Language
eng
Abstract
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.
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