非线性动力系统在线辨识的koopman鲁棒模型预测控制

IF 19.2 1区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-10-20 DOI:10.1109/JAS.2025.125546

Ruiqi Ke;Jingchuan Tang;Zongyu Zuo;Yan Shi

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引用次数: 0

摘要

这封信提出了一种数据驱动控制未知非线性系统的新方法。利用基于Koopman算子的在线稀疏识别，在线得到一个接近实际系统的高维线性系统模型。利用实时预测误差估计辨识出的模型与实际系统之间的误差上界，然后将其用于设计基于管的鲁棒模型预测控制器。通过数值仿真验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Koopman-Based Robust Model Predictive Control with Online Identification for Nonlinear Dynamical Systems

Dear Editor, This letter presents a novel approach to the data-driven control of unknown nonlinear systems. By leveraging online sparse identification based on the Koopman operator, a high-dimensional linear system model approximating the actual system is obtained online. The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error, which is then utilized in the design of a tube-based robust model predictive controller. The effectiveness of the proposed approach is validated by numerical simulation.

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来源期刊

Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering

CiteScore

23.50

自引率

11.00%

发文量

880

期刊介绍： The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.