Convergence Analysis for an Online Data-Driven Feedback Control Algorithm

Siming Liang (), Hui Sun, Richard Archibald and Feng Bao
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Siming Liang: Department of Mathematics, Florida State University, Tallahassee, FL 32304, USA
Hui Sun: Citigroup Inc., Wilmington, DE 19801, USA
Richard Archibald: Devision of Computational Science and Mathematics, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Feng Bao: Department of Mathematics, Florida State University, Tallahassee, FL 32304, USA

Mathematics, 2024, vol. 12, issue 16, 1-28

Abstract: This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system’s state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings.

Keywords: stochastic optimal control; nonlinear filtering; data driven; maximum principle; stochastic optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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