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Developments in Model-Based Optimization and Control : Distributed Control and Industrial Applications
Sorin Olaru (ed.); Alexandra Grancharova (ed.); Fernando Lobo Pereira (ed.)
Springer (2015)
Pehmeäkantinen kirja
97,90
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ostoskoriin kpl
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Difference Equations, Discrete Dynamical Systems and Applications : IDCEA 2022, Gif-sur-Yvette, France, June 18–22
Sorin Olaru (ed.); Jim Cushing (ed.); Saber Elaydi (ed.); René Lozi (ed.)
Springer (2024)
Kovakantinen kirja
172,80
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ostoskoriin kpl
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Set-theoretic Fault-tolerant Control in Multisensor Systems
Florin Stoican; Sorin Olaru
ISTE Ltd and John Wiley & Sons Inc (2013)
Kovakantinen kirja
154,00
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ostoskoriin kpl
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Mixed-Integer Representations in Control Design - Mathematical Foundations and Applications
Ionela Prodan; Florin Stoican; Sorin Olaru; Silviu-Iulian Niculescu
Springer International Publishing AG (2015)
Pehmeäkantinen kirja
49,60
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Developments in Model-Based Optimization and Control : Distributed Control and Industrial Applications
97,90 €
Springer
Sivumäärä: 381 sivua
Asu: Pehmeäkantinen kirja
Julkaisuvuosi: 2015, 24.12.2015 (lisätietoa)
Kieli: Englanti
Tuotesarja: Lecture Notes in Control and Information Sciences 464

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 decentralized and cooperative formulations of receding-horizon optimal control. Readers will find fourteen chapters dedicated to optimization-based tools for robustness analysis, and decision-making in relation to feedback mechanisms—fault detection, for example—and three chapters putting forward applications where the model-based optimization brings a novel perspective.

Developments in Model-Based Optimization and Control is a selection of contributions expanded and updated from the Optimisation-based Control and Estimation workshops held in November 2013 and November 2014. It forms a useful resource for academic researchers and graduate students interested in the state of the art in predictive control. Control engineers working in model-based optimization and control, particularly in its bioprocess applications will also find this collection instructive.




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