Stability and Convergence of a Randomized Model Predictive Control Strategy

Veldman D, Borkowski A, Zuazua Iriondo E (2024)

Publication Language: English

Publication Status: Accepted

Publication Type: Unpublished / Preprint

Future Publication Type: Article in Edited Volumes

Publication year: 2024

Publisher: IEEE Trans. Automat.

URI: https://arxiv.org/abs/2211.05463

Open Access Link: https://arxiv.org/abs/2211.05463

Abstract

This paper is concerned with a combination of Random Batch Methods (RBMs) and Model Predictive Control (MPC) called RBM-MPC. In RBM-MPC, the RBM is used to speed up the solution of the finite horizon optimal control problems that need to be solved in MPC. We analyze our algorithm in the linear quadratic setting and obtain explicit error estimates that characterize the stability and convergence of the proposed method. The obtained estimates are validated in numerical experiments that also demonstrate the effectiveness of RBM-MPC.

Authors with CRIS profile

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

How to cite

APA:

Veldman, D., Borkowski, A., & Zuazua Iriondo, E. (2024). Stability and Convergence of a Randomized Model Predictive Control Strategy. (Unpublished, Accepted).

MLA:

Veldman, Daniel, Alexandra Borkowski, and Enrique Zuazua Iriondo. Stability and Convergence of a Randomized Model Predictive Control Strategy. Unpublished, Accepted. 2024.

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