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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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发表于 2025-3-21 17:35:58 | 显示全部楼层 |阅读模式
书目名称Model Predictive Control for AC Motors
副标题Robustness and Accur
编辑Yaofei Han,Chao Gong,Jinqiu Gao
视频video
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
图书封面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
关键词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
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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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