Model Predictive Control in the Process Industry (Advances in Industrial Control

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
subject_code
TJFM
gpsr_safety_attestation
true
target_audience
General/trade
is_adult_product
false
edition_number
1
binding
paperback
edition
Softcover reprint of the original 1st ed. 1995
MPN
N/A
batteries_required
false
manufacturer
Springer
Brand
Springer
series_title
Advances in Industrial Control
number_of_items
1
pages
260
genre
Automatic control engineering
publication_date
2011-11-20T00:00:01Z
unspsc_code
55101500
batteries_included
false
ISBN
9781447130109
Category

About this product

Product Identifiers

Publisher
Springer London, The Limited
ISBN-10
1447130103
ISBN-13
9781447130109
eBay Product ID (ePID)
175929287

Product Key Features

Number of Pages
Xviii, 239 Pages
Language
English
Publication Name
Model Predictive Control in the Process Industry
Publication Year
2011
Subject
Software Development & Engineering / General, Electrical
Type
Textbook
Subject Area
Computers, Technology & Engineering
Author
Carlos A. Bordons, Eduardo F. Camacho
Series
Advances in Industrial Control Ser.
Format
Trade Paperback

Dimensions

Item Weight
14.2 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
Dewey Edition
20
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
629.8
Table Of Content
1 Introduction to Model Based Predictive Control.- 1.1 MPC Strategy.- 1.2 Historical Perspective.- 1.3 Outline of the chapters.- 2 Model Based Predictive Controllers.- 2.1 MPC Elements.- 2.2 Review of some MPC Algorithms.- 2.3 MPC Based on the Impulse Response.- 2.4 Generalized Predictive Control.- 2.5 Constrained Receding-Horizon Predictive Control.- 2.6 Stable GPC.- 2.7 Filter Polynomials for Improving Robustness.- 3 Simple Implementation of GPC for Industrial Processes.- 3.1 Plant Model.- 3.2 The Dead Time Multiple of Sampling Time Case.- 3.3 The Dead Time non Multiple of the Sampling Time Case.- 3.4 Integrating Processes.- 3.5 Consideration of Ramp Setpoints.- 4 Robustness Analysis in Precomputed GPC.- 4.1 Structured Uncertainties.- 4.2 Stability Limits with Structured Uncertainties.- 4.3 Unstructured Uncertainties.- 4.4 Relationship between the two Types of Uncertainties.- 4.5 General Comments.- 5 Multivariate GPC.- 5.1 Derivation of Multivariable GPC.- 5.2 Obtaining a Matrix Fraction Description.- 5.3 State Space Formulation.- 5.4 Dead Time Problems.- 5.5 Example: Distillation Column.- 6 Constrained MPC.- 6.1 Constraints and GPC.- 6.2 Revision of Main Quadratic Programming Algorithms.- 6.3 Constraints Handling.- 6.4 1-norm.- 6.5 Constrained MPC and Stability.- 7 Robust MPC.- 7.1 Process Models and Uncertainties.- 7.2 Objective Functions.- 7.3 Illustrative Examples.- 8 Applications.- 8.1 Solar Power Plant.- 8.2 Composition Control in an Evaporator.- 8.3 Pilot Plant.- A Revision of the Simplex method.- A.1 Equality Constraints.- A.2 Finding an Initial Solution.- A.3 Inequality Constraints.- B Model Predictive Control Simulation Program.- References.
Synopsis
Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.
LC Classification Number
TJ212-225

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