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Titlebook: Discrete-Time Adaptive Iterative Learning Control; From Model-Based to Ronghu Chi,Na Lin,Ruikun Zhang Book 2022 The Editor(s) (if applicab

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發(fā)表于 2025-3-21 19:16:19 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Discrete-Time Adaptive Iterative Learning Control
副標(biāo)題From Model-Based to
編輯Ronghu Chi,Na Lin,Ruikun Zhang
視頻videohttp://file.papertrans.cn/282/281192/281192.mp4
概述Focuses on discrete-time adaptive iterative learning control (DAILC).Proposes systematic procedures for design and analysis of model-based DAILC for parametric systems.Proposes systematic procedures f
叢書名稱Intelligent Control and Learning Systems
圖書封面Titlebook: Discrete-Time Adaptive Iterative Learning Control; From Model-Based to Ronghu Chi,Na Lin,Ruikun Zhang Book 2022 The Editor(s) (if applicab
描述This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
出版日期Book 2022
關(guān)鍵詞Iterative Learning Control; Adaptive Iterative Learning Control; Terminal Iterative Learning Control; D
版次1
doihttps://doi.org/10.1007/978-981-19-0464-6
isbn_softcover978-981-19-0466-0
isbn_ebook978-981-19-0464-6Series ISSN 2662-5458 Series E-ISSN 2662-5466
issn_series 2662-5458
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:27:45 | 只看該作者
Data-Driven DAILC for Nonlinear Nonaffine Systemsu and Tan .; Xu and Jin .; Choi and Lee .; Tayebi .; Chien and Yao .; Rotariu et?al. .; Chi et?al. .; Li et?al. .; Zhang and Li .; Zhang et?al. .) have been widely studied by introducing parametric adaptation law in the learning process. The DAILC method not only overcomes the iteration-varying refe
板凳
發(fā)表于 2025-3-22 00:56:03 | 只看該作者
Multi-Input Enhanced Data-Driven Discrete-Time Adaptive ILCe initial states and desired trajectories are iteratively varying. However, the IDL approach employed in Chap.?7 merely makes use of control information from an immediately past one sample instant even if the original system itself is of higher order in control input. In other words, the dynamic eff
地板
發(fā)表于 2025-3-22 04:52:11 | 只看該作者
Data-Driven Discrete-Time Adaptive ILC for Terminal Tracking. For example, if the realistic plant can be modeled by a parametric system, the DAILC presented in Chaps. . and . can be applied. If the real plant contains some hard nonlinearities, the DAILC methods-based nonlinearity estimator or neural networks presented in Chaps. . and . may be a proper select
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發(fā)表于 2025-3-22 12:13:10 | 只看該作者
Discrete-Time Adaptive Iterative Learning Control978-981-19-0464-6Series ISSN 2662-5458 Series E-ISSN 2662-5466
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發(fā)表于 2025-3-22 14:05:35 | 只看該作者
Dimensions of Sustainability Appraisal, Learning is an important feature of artificial intelligence (AI) to mimic the cognitive functions of humans. Learning is a broad concept and can be defined as a modification of a behavioral tendency by experience. In the cybernetics field, learning means to tune the control action to improve contro
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