# Stability and Convergence of a Randomized Model Predictive Control Strategy

arXiv: https://arxiv.org/abs/2211.05463 · HTML: https://mlsanity.com/paper/2211.05463/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Machine Learning and Control: Foundations, Advances, and Perspectives](https://mlsanity.com/paper/2510.03303/) (2025)
- [Convergence, design and training of continuous-time dropout as a random batch method](https://mlsanity.com/paper/2510.13134/) (2025)
- [Long-Time Trajectory Approximation via SA-NODEs: Model Predictive and Floquet Strategies](https://mlsanity.com/paper/2608.10738/) (2026)

## Abstract (arXiv)

RBM-MPC is a computationally efficient variant of Model Predictive Control (MPC) in which the Random Batch Method (RBM) is used to speed up the finite-horizon optimal control problems at each iteration. In this paper, stability and convergence estimates are derived for RBMMPC of unconstrained linear systems. The obtained estimates are validated in a numerical example that also shows a clear computational advantage of RBM-MPC.