Stability and Convergence of a Randomized Model Predictive Control Strategy

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 article, stability and convergence estimates are derived for RBM-MPC of unconstrained lin...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2024-09, Vol.69 (9), p.6253-6260
Main Authors: Veldman, Daniel W. M., Borkowski, Alexandra, Zuazua, Enrique
Format: Article
Language:English
Subjects:
Approximation algorithms
Computational efficiency
Convergence
Error estimates
model predictive control (MPC)
Numerical stability
random batch method (RBM)
Read only memory
receding horizon control
stability
Stability criteria
Vectors
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Summary: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 article, 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.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2024.3375253