Closed-loop data-enabled predictive control and its equivalence with closed-loop subspace predictive control

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica Pub Date : 2025-09-07 DOI:10.1016/j.automatica.2025.112556

Rogier Dinkla , Tom Oomen , Sebastiaan Paul Mulders , Jan-Willem van Wingerden

{"title":"Closed-loop data-enabled predictive control and its equivalence with closed-loop subspace predictive control","authors":"Rogier Dinkla , Tom Oomen , Sebastiaan Paul Mulders , Jan-Willem van Wingerden","doi":"10.1016/j.automatica.2025.112556","DOIUrl":null,"url":null,"abstract":"<div><div>Factors like growing data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Time marching simulations of DeePC and CL-DeePC are conducted using Hankel matrices of past data that are updated at every time step to induce potentially troublesome closed-loop correlations between inputs and noise. Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"183 ","pages":"Article 112556"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatica","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0005109825004510","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Factors like growing data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Time marching simulations of DeePC and CL-DeePC are conducted using Hankel matrices of past data that are updated at every time step to induce potentially troublesome closed-loop correlations between inputs and noise. Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.

查看原文本刊更多论文

闭环数据使能预测控制及其与闭环子空间预测控制的等价性

不断增长的数据可用性和不断增加的系统复杂性等因素引发了人们对数据驱动预测控制（DDPC）方法的兴趣，比如数据支持预测控制（DeePC）。然而，在存在噪声的情况下会产生闭环识别偏差，从而降低所获得的控制策略的有效性。在本文中，我们提出了闭环数据支持预测控制（CL-DeePC），这是一个统一不同方法来应对这一挑战的框架。为此，CL-DeePC结合了工具变量（IVs）来合成并依次应用一致的单步或多步预测因子。此外，开发了一种计算效率高的CL-DeePC实现，揭示了与闭环子空间预测控制（CL-SPC）的等价性。DeePC和CL-DeePC的时间推进模拟使用过去数据的Hankel矩阵进行，该矩阵在每个时间步更新，以诱导输入和噪声之间潜在的麻烦的闭环相关性。与DeePC相比，CL-DeePC模拟显示了更好的参考跟踪，灵敏度研究发现，噪声引起的参考跟踪性能下降的敏感性降低了48%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Automatica 工程技术-工程：电子与电气

CiteScore

10.70

自引率

7.80%

发文量

617

审稿时长

5 months

期刊介绍： Automatica is a leading archival publication in the field of systems and control. The field encompasses today a broad set of areas and topics, and is thriving not only within itself but also in terms of its impact on other fields, such as communications, computers, biology, energy and economics. Since its inception in 1963, Automatica has kept abreast with the evolution of the field over the years, and has emerged as a leading publication driving the trends in the field. After being founded in 1963, Automatica became a journal of the International Federation of Automatic Control (IFAC) in 1969. It features a characteristic blend of theoretical and applied papers of archival, lasting value, reporting cutting edge research results by authors across the globe. It features articles in distinct categories, including regular, brief and survey papers, technical communiqués, correspondence items, as well as reviews on published books of interest to the readership. It occasionally publishes special issues on emerging new topics or established mature topics of interest to a broad audience. Automatica solicits original high-quality contributions in all the categories listed above, and in all areas of systems and control interpreted in a broad sense and evolving constantly. They may be submitted directly to a subject editor or to the Editor-in-Chief if not sure about the subject area. Editorial procedures in place assure careful, fair, and prompt handling of all submitted articles. Accepted papers appear in the journal in the shortest time feasible given production time constraints.