Predictive Approaches to Control of Complex Systems (Record no. 54541)

000 -LEADER
fixed length control field 03586nam a22005055i 4500
001 - CONTROL NUMBER
control field 978-3-642-33947-9
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421111653.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 120920s2013 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783642339479
-- 978-3-642-33947-9
082 04 - CLASSIFICATION NUMBER
Call Number 629.8
100 1# - AUTHOR NAME
Author Karer, Gorazd.
245 10 - TITLE STATEMENT
Title Predictive Approaches to Control of Complex Systems
300 ## - PHYSICAL DESCRIPTION
Number of Pages XII, 260 p.
490 1# - SERIES STATEMENT
Series statement Studies in Computational Intelligence,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- Modeling of complex systems for predictive control -- Modeling an identification of a batch reactor -- Predictive control of complex systems -- Predictive control of complex systems.
520 ## - SUMMARY, ETC.
Summary, etc A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems.
700 1# - AUTHOR 2
Author 2 Škrjanc, Igor.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-33947-9
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2013.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- System theory.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Complexity, Computational.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Control engineering.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Control.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Complexity.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Systems Theory, Control.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1860-949X ;
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-- ZDB-2-ENG

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