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Titlebook: Model Predictive Control; Classical, Robust an Basil Kouvaritakis,Mark Cannon Textbook 2016 Springer International Publishing AG, part of S

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發(fā)表于 2025-3-21 17:57:33 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Model Predictive Control
副標(biāo)題Classical, Robust an
編輯Basil Kouvaritakis,Mark Cannon
視頻videohttp://file.papertrans.cn/636/635781/635781.mp4
概述Equips the student to deal with broad classes of system uncertainties with the first textbook treatment of stochastic predictive control.Gives the student an up-to-date source on robust predictive con
叢書名稱Advanced Textbooks in Control and Signal Processing
圖書封面Titlebook: Model Predictive Control; Classical, Robust an Basil Kouvaritakis,Mark Cannon Textbook 2016 Springer International Publishing AG, part of S
描述.For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques...Model Predictive Control .describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides:...extensive use of illustrative examples;..sample problems; and..discussion of novel control applications such asr
出版日期Textbook 2016
關(guān)鍵詞Constained Systems; Controller Parameterization; Convex Optimization; Model Predictive Control Textbook
版次1
doihttps://doi.org/10.1007/978-3-319-24853-0
isbn_softcover978-3-319-79689-5
isbn_ebook978-3-319-24853-0Series ISSN 1439-2232 Series E-ISSN 2510-3814
issn_series 1439-2232
copyrightSpringer International Publishing AG, part of Springer Nature 2016
The information of publication is updating

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1439-2232 onstraints for the cases of multiplicative and stochastic model uncertainty. The book provides:...extensive use of illustrative examples;..sample problems; and..discussion of novel control applications such asr978-3-319-79689-5978-3-319-24853-0Series ISSN 1439-2232 Series E-ISSN 2510-3814
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Basil Kouvaritakis,Mark CannonEquips the student to deal with broad classes of system uncertainties with the first textbook treatment of stochastic predictive control.Gives the student an up-to-date source on robust predictive con
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https://doi.org/10.1007/978-3-319-24853-0Constained Systems; Controller Parameterization; Convex Optimization; Model Predictive Control Textbook
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https://doi.org/10.1007/978-94-024-0858-4e issues. This guide empowers the development team with the experience of Social Sciences in these issues. This paper introduces the guide and shows its application in a case study about a web application.
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