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.
@article{arxiv.2211.05463,
title = {Stability and Convergence of a Randomized Model Predictive Control Strategy},
author = {Daniël Veldman and Alexandra Borkowski and Enrique Zuazua},
journal= {arXiv preprint arXiv:2211.05463},
year = {2024}
}