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Convergence Analysis for an Online Data-Driven Feedback Control Algorithm

Author

Listed:
  • 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)

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.

Suggested Citation

  • Siming Liang & Hui Sun & Richard Archibald & Feng Bao, 2024. "Convergence Analysis for an Online Data-Driven Feedback Control Algorithm," Mathematics, MDPI, vol. 12(16), pages 1-28, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2584-:d:1461018
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    References listed on IDEAS

    as
    1. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
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