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Titlebook: Developments in Model-Based Optimization and Control; Distributed Control Sorin Olaru,Alexandra Grancharova,Fernando Lobo Pe Book 2015 Spr

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发表于 2025-3-21 19:53:08 | 显示全部楼层 |阅读模式
书目名称Developments in Model-Based Optimization and Control
副标题Distributed Control
编辑Sorin Olaru,Alexandra Grancharova,Fernando Lobo Pe
视频video
概述Gives readers a series of novel insights into receding-horizon optimal control allowing them to keep abreast of developments across the whole area.Provides information on various bioprocess-related ap
丛书名称Lecture Notes in Control and Information Sciences
图书封面Titlebook: Developments in Model-Based Optimization and Control; Distributed Control  Sorin Olaru,Alexandra Grancharova,Fernando Lobo Pe Book 2015 Spr
描述.This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design..Developments in Model-Based Optimization and Control. takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization..The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on:.· complexity and structure in model predictive control (MPC);.· collaborative MPC;.· distributed MPC;.· optimization-based analysis and design; and.· applications to bioprocesses, multivehicle systems or energy management..The various contributions cover a subject spectrum including inverse optimality and more modern
出版日期Book 2015
关键词Bioprocesses; Constrained Estimation; Constrained and Predictive Control; Control of Impulsive Systems;
版次1
doihttps://doi.org/10.1007/978-3-319-26687-9
isbn_softcover978-3-319-26685-5
isbn_ebook978-3-319-26687-9Series ISSN 0170-8643 Series E-ISSN 1610-7411
issn_series 0170-8643
copyrightSpringer International Publishing Switzerland 2015
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https://doi.org/10.1007/978-3-319-26687-9Bioprocesses; Constrained Estimation; Constrained and Predictive Control; Control of Impulsive Systems;
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https://doi.org/10.1007/978-3-642-01546-5ex problems which can arise in real-time applications, such as model predictive control. When it is difficult to project on the primal constraint set described by a collection of general convex functions, we use the Lagrangian relaxation to handle the complicated constraints and then, we apply dual
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Willi Freeden,Michael Schreinerto be relevant in explicit model predictive control (MPC) design in terms of reducing the prediction horizon to at most two steps. In view of practical applications, typically leading to problems that are not directly invertible, we show how to adapt the inverse optimality to specific, possibly conv
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Geodetic Boundary Value Problemnd input constraints is proposed. The robustness is defined in terms of the optimization of a cost function accumulated over the uncertainty and satisfying state constraints for a finite subset of uncertainties. The approach reformulates the original centralized robust MPC problem into a quadratic p
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https://doi.org/10.1007/978-3-642-54551-1tems, typically considered as large-scale systems, can include different dynamical and functional characteristics in both, generators and loads. In this sense, the use of distributed-coordinated control strategies, including operational constraints, becomes an interesting alternative for these appli
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