Fast Data-Driven Predictive Control for LTI Systems: A Randomized Approach

被引：0

作者：

Kedia, Vatsal ^{[1
]}

George, Sneha Susan ^{[1
]}

Chakraborty, Debraj ^{[1
]}

机构：

[1] Indian Inst Technol, Dept Elect Engn, Mumbai 400076, India

来源：

IEEE CONTROL SYSTEMS LETTERS | 2024年 / 8卷

关键词：

Trajectory; Predictive control; Optimization; Linear systems; Data models; Computational modeling; Computational efficiency; Vectors; Training; Standards; Data-driven control; predictive control for linear systems; randomized algorithms; ALGORITHMS;

D O I：

10.1109/LCSYS.2025.3542684

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this letter, the problem of reducing the computational complexity of a recently developed data-driven predictive control scheme is considered. For this purpose, a randomized data compression technique is proposed, which makes the dimension of the decision variable independent of the recorded data size, thereby reducing the complexity of the online optimization problems in data-driven predictive control to that of classical model-based predictive control. The proposed method outperforms other competing complexity reduction schemes in benchmark tests, while guaranteeing similar control performance and stability properties.

引用

页码：3416 / 3421

页数：6

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