  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.
