Computationally Efficient Model Predictive Control Algorithms (Record no. 54557)

000 -LEADER
fixed length control field 03986nam a22004935i 4500
001 - CONTROL NUMBER
control field 978-3-319-04229-9
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421111653.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 140124s2014 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319042299
-- 978-3-319-04229-9
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author �awryńczuk, Maciej.
245 10 - TITLE STATEMENT
Title Computationally Efficient Model Predictive Control Algorithms
Sub Title A Neural Network Approach /
300 ## - PHYSICAL DESCRIPTION
Number of Pages XXIV, 316 p. 87 illus.
490 1# - SERIES STATEMENT
Series statement Studies in Systems, Decision and Control,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 MPC Algorithms -- MPC Algorithms Based on Double-Layer Perceptron Neural Models: the Prototypes -- MPC Algorithms Based on Neural Hammerstein and Wiener Models -- MPC Algorithms Based on Neural State-Space Models -- MPC Algorithms Based on Neural Multi-Models -- MPC Algorithms with Neural Approximation -- Stability and Robustness of MPC Algorithms -- Cooperation Between MPC Algorithms and Set-Point Optimisation Algorithms.
520 ## - SUMMARY, ETC.
Summary, etc This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: �         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. �         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. �         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). �         The MPC algorithms with neural approximation with no on-line linearization. �         The MPC algorithms with guaranteed stability and robustness. �         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-04229-9
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2014.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
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-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Control engineering.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Control.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence (incl. Robotics).
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2198-4182 ;
912 ## -
-- ZDB-2-ENG

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