Learning-Enabled Control: Statistical Foundations and Architectures Charis Stamouli

Format:

Book

Thesis/Dissertation

Author/Creator:

Stamouli, Charis, author.

Contributor:

University of Pennsylvania. Electrical and Systems Engineering., degree granting institution.

Language:

English

Subjects (All):

0790.

0537.

0463.

Local Subjects:

0790.

0537.

0463.

Genre:

Academic theses

Physical Description:

1 electronic resource (348 pages)

Contained In:

Dissertations Abstracts International 88-03B

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2026

Language Note:

English

Summary:

Recent advances in learning-enabled control have dramatically expanded the capabilities of autonomous systems, with applications ranging from robots and self-driving cars to industrial automation systems. However, reliable real-world deployment remains fundamentally constrained by the uncertainty in learned components, the complexity of large-scale systems, and the high cost of collecting system data. Toward addressing these limitations, this thesis advances the statistical foundations and control architectures underlying learning-enabled autonomy. We begin with the problem of learning nonlinear systems, deriving finite-sample guarantees for the identification of time-varying systems and the selection of the model class from which the system is learned. We then establish statistical safety guarantees and certified mission completion for learning-enabled model predictive controllers under unknown system noise and stochastic external agents. Next, we broaden the scope from single-layer controllers to layered control architectures, co-designing layers operating at different timescales with progressively more detailed system models, while accounting for constraints, stochastic noise, and partial observations. Finally, we shift from a single control task to multitask architectures, where a common controller is learned over a distribution of related systems and control missions. For linear quadratic control, we establish performance and generalization guarantees for multitask policy optimization via novel system-theoretic measures of task heterogeneity

Notes:

Source: Dissertations Abstracts International, Volume: 88-03, Section: B.

Advisors: Pappas, George J. Committee members: Matni, Nikolai; Anderson, James

Ph.D. University of Pennsylvania 2026

Includes bibliographical references

Local Notes:

School code: 0175

ISBN:

9798190900057

Access Restriction:

Restricted for use by site license