Dual-variable iterative learning control for switched systems with iteration experience succession strategy

IF 4.8 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yiwen Qi , Caibin Yao , Choon Ki Ahn , Dong Shen , Ziyu Qu
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引用次数: 0

Abstract

Iterative learning control (ILC) refers to the optimal control of repetitive systems. It is a thorny problem that ILC is maliciously terminated during any single run, resulting in different running lengths. In addition, reinforcement learning is an intelligent approach to finding the optimal controller gains for system performance improvement. In this paper, dual variable ILC (DV-ILC) for switched systems with arbitrary switching rules is examined. First, for variable iteration run lengths induced by security issues, an iteration experience succession strategy (IESS) is proposed, and the minimum number of iterations is presented. Second, a reinforcement learning optimizer is adopted to continuously regulate variable controller gains. The controller is designed in the form of an open-loop P-type and a closed-loop PD-type working together, meaning that both current and historical information can be fully utilized. In addition, the tracking error convergence in the iteration domain is proved. Finally, the simulations prove the effectiveness of the proposed method.
基于迭代经验连续策略的切换系统双变量迭代学习控制
迭代学习控制(ILC)是指对重复系统的最优控制。ILC在任何一次下入过程中都会被恶意终止,从而导致不同的下入长度,这是一个棘手的问题。此外,强化学习是一种寻找最优控制器增益以提高系统性能的智能方法。本文研究了具有任意开关规则的开关系统的对偶变量ILC (DV-ILC)。首先,针对安全问题导致的迭代运行长度变化,提出了迭代经验继承策略,并给出了最小迭代次数。其次,采用强化学习优化器对变量控制器增益进行连续调节。控制器设计为开环p型和闭环pd型共同工作的形式,这意味着可以充分利用当前和历史信息。此外,还证明了跟踪误差在迭代域的收敛性。最后,通过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。
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来源期刊
Automatica
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.
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