| 书目名称 | Iterative Learning Control | | 副标题 | Robustness and Monot | | 编辑 | Hyo-Sung Ahn,YangQuan Chen,Kevin L. Moore | | 视频video | | | 概述 | Shows the reader how to use robust iterative learning control in the face of model uncertainty.Helps to improve the performance of repetitive electromechanical tasks, widespread in industry.Provides a | | 丛书名称 | Communications and Control Engineering | | 图书封面 |  | | 描述 | This monograph studies the design of robust, monotonically-convergent it- ative learning controllers for discrete-time systems. Iterative learning control (ILC) is well-recognized as an e?cient method that o?ers signi?cant p- formance improvement for systems that operate in an iterative or repetitive fashion (e. g. , robot arms in manufacturing or batch processes in an industrial setting). Though the fundamentals of ILC design have been well-addressed in the literature, two key problems have been the subject of continuing - search activity. First, many ILC design strategies assume nominal knowledge of the system to be controlled. Only recently has a comprehensive approach to robust ILC analysis and design been established to handle the situation where the plant model is uncertain. Second, it is well-known that many ILC algorithms do not produce monotonic convergence, though in applications monotonic convergencecan be essential. This monograph addresses these two keyproblems by providingauni?ed analysisanddesignframeworkforrobust, monotonically-convergent ILC. The particular approach used throughout is to consider ILC design in the iteration domain, rather than in the time domain. U | | 出版日期 | Book 2007 | | 关键词 | Kalman-Filter; algorithms; learning; linear optimization; robot; robotics; uncertainty | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-84628-859-3 | | isbn_softcover | 978-1-84996-658-0 | | isbn_ebook | 978-1-84628-859-3Series ISSN 0178-5354 Series E-ISSN 2197-7119 | | issn_series | 0178-5354 | | copyright | Springer-Verlag London 2007 |
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发表于 2025-3-21 17:12:48
