Stability and Convergence of a Randomized Model Predictive Control Strategy

被引：2

作者：

Veldman, Daniel W. M. ^{[1
]}

Borkowski, Alexandra ^{[2
]}

Zuazua, Enrique ^{[1
,3
,4
]}

机构：

[1] Friedrich Alexander Univ Erlangen Nurnberg, Chair Dynam Control Machine Learning & Numer Alexa, Dept Math, D-91058 Erlangen, Germany

[2] Kings Coll London, Dept Math, London WC2R 2LS, England

[3] Fdn Deusto, Chair Computat Math, Bilbao 48007, Spain

[4] Univ Autonoma Madrid, Dept Matemat, Madrid 28049, Spain

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2024年 / 69卷 / 09期

关键词：

Numerical stability; Convergence; Stability criteria; Read only memory; Computational efficiency; Approximation algorithms; Vectors; Error estimates; model predictive control (MPC); random batch method (RBM); receding horizon control; stability; TIME;

D O I：

10.1109/TAC.2024.3375253

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：6253 / 6260

页数：8

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