Stability and Convergence of a Randomized Model Predictive Control Strategy

TL;DR

This paper analyzes the stability and convergence of RBM-MPC, a fast variant of MPC using the Random Batch Method, demonstrating theoretical guarantees and computational benefits for linear systems.

Contribution

It provides the first stability and convergence estimates for RBM-MPC applied to unconstrained linear systems.

Findings

Theoretical stability and convergence bounds are established.

Numerical example confirms the effectiveness of RBM-MPC.

RBM-MPC offers computational advantages over traditional MPC.

Abstract

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.