Stability and Convergence of a Randomized Model Predictive Control Strategy

Veldman D, Borkowski A, Zuazua E (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024

Journal

IEEE Transactions on Automatic Control

Pages Range: 1-8

DOI: 10.1109/TAC.2024.3375253

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.

Authors with CRIS profile

Daniël Willem Maria Veldman Lehrstuhl für Dynamics, Control, Machine Learning and Numerics (Alexander von Humboldt-Professur) Enrique Zuazua Iriondo

Involved external institutions

King’s College London GB United Kingdom (GB) University of Deusto / Universidad de Deusto ES Spain (ES)

How to cite

APA:

Veldman, D., Borkowski, A., & Zuazua, E. (2024). Stability and Convergence of a Randomized Model Predictive Control Strategy. IEEE Transactions on Automatic Control, 1-8. https://doi.org/10.1109/TAC.2024.3375253

MLA:

Veldman, Daniel, Alexandra Borkowski, and Enrique Zuazua. "Stability and Convergence of a Randomized Model Predictive Control Strategy." IEEE Transactions on Automatic Control (2024): 1-8.

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