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Convergence Analysis for an Online Data-Driven Feedback Control Algorithm

Liang, Siming (ORCID:000900029652795X)·Sun, Hui·Archibald, Richard (ORCID:0000000245389780)·Bao, Feng
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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.

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97 MATHEMATICS AND COMPUTINGData drivenMaximum principleNonlinear filteringStochastic optimal controlStochastic optimization

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Liang, Siming (ORCID:000900029652795X), Sun, Hui, Archibald, Richard (ORCID:0000000245389780), Bao, Feng. 2024-08-21. Convergence Analysis for an Online Data-Driven Feedback Control Algorithm. https://doi.org/10.3390/math12162584

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