1. Convergence Analysis for an Online Data-Driven Feedback Control Algorithm.
- Author
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Liang, Siming, Sun, Hui, Archibald, Richard, and Bao, Feng
- Subjects
STOCHASTIC control theory ,STOCHASTIC analysis ,STOCHASTIC convergence ,KALMAN filtering ,ALGORITHMS - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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