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Titlebook: Model Predictive Control for AC Motors; Robustness and Accur Yaofei Han,Chao Gong,Jinqiu Gao Book 2022 The Editor(s) (if applicable) and Th

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發(fā)表于 2025-3-21 17:35:58 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Model Predictive Control for AC Motors
副標(biāo)題Robustness and Accur
編輯Yaofei Han,Chao Gong,Jinqiu Gao
視頻videohttp://file.papertrans.cn/636/635785/635785.mp4
概述Includes in-depth discussions on accuracy and robustness of model predictive control.Covers special topics illustrating the accuracy of model predictive control.Includes systematical approaches solvin
圖書封面Titlebook: Model Predictive Control for AC Motors; Robustness and Accur Yaofei Han,Chao Gong,Jinqiu Gao Book 2022 The Editor(s) (if applicable) and Th
描述.This book introduces how to improve the accuracy and robustness of model predictive control. Firstly, the disturbance observation- and compensation-based method is developed. Secondly, direct parameter identification methods are developed. Thirdly, the seldom-focused-on issues such as sampling and delay problems are solved in this book. Overall, this book solves the problems in a systematic and innovative way...Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
出版日期Book 2022
關(guān)鍵詞AC motors; model predictive control; robustness; prediction accuracy; sliding mode observers; Open Access
版次1
doihttps://doi.org/10.1007/978-981-16-8066-3
isbn_softcover978-981-16-8068-7
isbn_ebook978-981-16-8066-3
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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Thirdly, the seldom-focused-on issues such as sampling and delay problems are solved in this book. Overall, this book solves the problems in a systematic and innovative way...Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.978-981-16-8068-7978-981-16-8066-3
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978-981-16-8068-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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https://doi.org/10.1007/978-981-16-8066-3AC motors; model predictive control; robustness; prediction accuracy; sliding mode observers; Open Access
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On Normative Reinforcement Learning via?Safe Reinforcement Learningts. We analyze some of the difficulties that arise from attempting to represent norms with LTL, provide an algorithm for synthesizing LTL specifications from certain normative systems, and analyze its power and limits with a case study.
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