Data informativity for tracking control of learning systems: Test and design conditions

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Automatica Pub Date : 2024-09-07 DOI:10.1016/j.automatica.2024.111885

Yuxin Wu, Deyuan Meng

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Abstract

How to develop control design methods directly based on the input and output data, instead of utilizing the system model, becomes a practically important topic in the control community. This paper explores the data informativity for the data-based control design methods, with a special focus on accomplishing the tracking objective for iterative learning control (ILC) systems. With the data collected under a certain test framework for ILC systems, a necessary and sufficient condition on the data informativity is provided for trackability of the desired reference, which is a premise for the realization of the perfect tracking objective. Based on the informative data for trackability, three classes of data-based ILC updating laws are designed to reach the perfect tracking objective for a group of ILC systems compatible with the informative data. Moreover, the data informativity for $δ$ -trackability of the desired reference is discussed with the focus on the more general $δ$ -tracking objective, under which a data-based ILC updating law is presented by only resorting to the informative data for $δ$ -trackability. In addition, for ILC systems with noises, the collected noisy data are leveraged to further exploit an ILC updating law to achieve the robust tracking objective. All the developed data-based ILC updating laws are applicable for any linear ILC system despite whether it is irregular or not, where the specific system model is not required.

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学习系统跟踪控制的数据信息化：测试和设计条件

如何直接基于输入和输出数据，而不是利用系统模型来开发控制设计方法，成为控制界一个重要的实际课题。本文探讨了基于数据的控制设计方法的数据信息性，并特别关注完成迭代学习控制（ILC）系统的跟踪目标。通过在 ILC 系统的特定测试框架下收集的数据，为所需参照物的可跟踪性提供了数据信息性的必要条件和充分条件，这是实现完美跟踪目标的前提。根据可跟踪性的信息数据，设计了三类基于数据的 ILC 更新规律，以达到与信息数据兼容的一组 ILC 系统的完美跟踪目标。此外，还讨论了所需参照物的δ-可跟踪性的数据信息性，重点是更一般的δ-跟踪目标，在这种情况下，只需求助于δ-可跟踪性的信息数据，就能提出基于数据的 ILC 更新规律。此外，对于有噪声的 ILC 系统，收集到的噪声数据可进一步利用 ILC 更新法来实现鲁棒跟踪目标。所有开发的基于数据的 ILC 更新法则都适用于任何线性 ILC 系统，无论它是否不规则，而且不需要特定的系统模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.