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Data-driven control of stochastic systems: representation, prediction, and optimal control
Citation Link: https://doi.org/10.15480/882.16328
Publikationstyp
Doctoral Thesis
Date Issued
2025
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2025-06-06
Institute
TORE-DOI
Citation
Verlag Dr. Hut 978-3-8439-5694-9: (2025)
Publisher
Verlag Dr. Hut
ISBN
978-3-8439-5694-9
This thesis develops a unified framework for data-driven control of stochastic systems, combining the fundamental lemma of Jan C. Willems and Polynomial Chaos Expansion (PCE) to address representation, prediction, and control of stochastic systems without parametric models. It extends Willems' Fundamental Lemma to stochastic systems, enabling representing stochastic system behavior using recorded input/output/disturbance data. By condensing unmeasured or unmodeled disturbances into a residual disturbance that can be estimated and partially statistically modeled, the proposed data-driven stochastic prediction scheme ensures effective predictions with PCE-based confidence intervals. The thesis also investigates stochastic optimal control, introducing a data-driven output-feedback scheme with closed-loop guarantees.
Subjects
Data-driven control
Stochastic control
Model predictive control
Polynomial chaos expansion
Uncertainty propagation
Willems' fundamental lemma
DDC Class
519: Applied Mathematics, Probabilities
621.3: Electrical Engineering, Electronic Engineering
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Pan_Guanru_Data-Driven_Control_of_Stochastic Systems.pdf
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