# Ensemble Kalman-Bucy filtering for nonlinear model predictive control

arXiv: https://arxiv.org/abs/2503.12474 · HTML: https://mlsanity.com/paper/2503.12474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale](https://mlsanity.com/paper/2012.05841/) (2020)
- [Ensemble Kalman Methods: A Mean Field Perspective](https://mlsanity.com/paper/2209.11371/) (2022)
- [Continuous time Stochastic optimal control under discrete time partial observations](https://mlsanity.com/paper/2407.18018/) (2024)

## Built on by (most cited first, 1 of 1)

- [Digital Twins: McKean-Pontryagin Control for Partially Observed Physical Twins](https://mlsanity.com/paper/2510.00937/) (2025)

## Abstract (arXiv)

We consider the problem of optimal control for partially observed dynamical systems. Despite its prevalence in practical applications, there are still very few algorithms available, which take uncertainties in the current state estimates and future observations into account. In other words, most current approaches separate state estimation from the optimal control problem. In this paper, we extend the popular ensemble Kalman filter to receding horizon optimal control problems in the spirit of nonlinear model predictive control. We provide an interacting particle approximation to the forward-backward stochastic differential equations arising from Pontryagin's maximum principle with the forward stochastic differential equation provided by the time-continuous ensemble Kalman-Bucy filter equations. The receding horizon control laws are approximated as linear and are continuously updated as in nonlinear model predictive control. We illustrate the performance of the proposed methodology for an inverted pendulum example.