Stability and Convergence of a Randomized Model Predictive Control Strategy

• 成果类型: Article
• 署名作者: Veldman, Daniel W. M.; Borkowski, Alexandra; Zuazua, Enrique
• 署名单位: University of Erlangen Nuremberg; University of London; King's College London; University of Deusto; Autonomous University of Madrid
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3375253
• 发表日期: 2024
• 页码: 6253-6260
• 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

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.