Stability and Convergence of a Randomized Model Predictive Control Strategy

Daniël Veldman, Alexandra Borkowski, Enrique Zuazua

Research output: Contribution to journalArticlepeer-review

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 RBM-MPC of unconstrained linear systems. The obtained estimates are validated in a numerical example that also shows a clear computational advantage of RBM-MPC.
Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalIEEE Transactions on Automatic Control
Early online date8 Mar 2024
DOIs
Publication statusE-pub ahead of print - 8 Mar 2024

Fingerprint

Dive into the research topics of 'Stability and Convergence of a Randomized Model Predictive Control Strategy'. Together they form a unique fingerprint.

Cite this