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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
This book presents the most important methods used for the design of digital controls implemented in industrial applications. The best modelling and identification techniques for dynamical systems are presented as well as the algorithms for the implementation of the modern solutions of process control. The proposed described methods are illustrated by various case studies for the main industrial sectors
There exist a number of books related each one to a single type of control, yet usually without comparisons for various industrial sectors. Some other books present modelling and identification methods or signal processing. This book presents the methods to solve all the problems linked to the design of a process control without the need to find additional information.
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Table of Contents
Cover
Title
Copyright
Preface
List of Acronyms and Notations
1 Introduction – Models and Dynamic Systems
1.1. Overview
1.2. Industrial process modeling
1.3. Model classes
2 Linear Identification of Closed-Loop Systems
2.1. Overview of system identification
2.2. Framework
2.3. Preliminary identification of a CL process
2.4. CLOE class of identification methods
2.5. Application: identification of active suspension
3 Digital Control Design Using Pole Placement
3.1. Digital proportional-integral-derivative algorithm control
3.2. Digital polynomial RST control
3.3. RST control by pole placement
3.4. Predictive RST control
4 Adaptive Control and Robust Control
4.1. Adaptive polynomial control systems
4.2. Robust polynomial control systems
5 Multimodel Control
5.1. Construction of multimodels
5.2. Stabilization and control of multimodels
5.3. Design of multimodel command: fuzzy approach
5.4. Trajectory tracking
6 Ill-Defined and/or Uncertain Systems
6.1. Study of the stability of nonlinear systems from vector norms
6.2. Adaptation of control
6.3. Overvaluation of the maximum error for various applications
6.4. Fuzzy secondary loop control
7 Modeling and Control of an Elementary Industrial Process
7.1. Modeling and control of fluid transfer processes
7.2. Modeling and controlling liquid storage processes
7.3. Modeling and controlling the storage process of a pneumatic capacitor
7.4. Modeling and controlling heat transfer processes
7.5. Modeling and control of component transfer processes
8 Industrial Applications – Case Studies
8.1. Digital control for an installation of air heating in a steel plant
8.2. Control and optimization of an ethylene installation
8.3. Digital control of a thermoenergy plant
8.4. Extremal control of a photovoltaic installation
Appendix A: Matrix Transformation from Any Representation to the Companion Form or Arrow Form
A1.1. Transition from a companion matrix to an arrow form matrix
A1.2. Direct transition of a matrix of any form to an arrow form
Appendix B: Determination of the Maximum Error for Pole Placement for a Nonlinear Third-Order Process
Appendix C: Determining the Attractor in a Nonlinear Process Controlled by Linear Decoupling
Appendix D: Overvaluation of the Maximum Error in a Tracking Problem for a Lur’e Postnikov Type Process
Blibliography
Index
End User License Agreement
List of Tables
8 Industrial Applications – Case Studies
Table 8.1.
Characteristic parameters of the photovoltaic panel
Table 8.2.
Parameters obtained for the 2-D model
Guide
Cover
Table of Contents
Begin Reading
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Series EditorBernard Dubuisson
Process Control Design for Industrial Applications
Dumitru Popescu
Amira Gharbi
Dan Stefanoiu
Pierre Borne
First published 2017 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2017
The rights of Dumitru Popescu, Amira Gharbi, Dan Stefanoiu and Pierre Borne to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2017930552
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-014-0
Preface
The purpose of this book is to present the various aspects and the different approaches most commonly employed in the control of industrial processes.
Considering that process control design is carried out using a model based approach, the modeling and identification of the systems are presented with the main objective of producing dynamic control models.
Using the chosen model, the control system is determined so as to ensure that the process satisfies the required level of performance. In the case of linear models, the main methods used in control design are based on the notion of pole placement.
In order to account for the fact that the chosen model is only a simplified and often imperfect description of the process’ behavior, more elaborate controls can be suggested: adaptive control, predictive control, internal model control, etc.
When the behavior of the process is strongly nonlinear, the use of a multimodel control can become necessary. The determination, choice and consideration of the various models that can describe the evolution of the process at various operating points depend on the validity of each of these models at the chosen operating points.
We propose a method for estimating the error induced by the models’ own estimation difficulties, and by the presence of uncertainties, noise and bounded perturbations.
After presenting the physical laws that govern the evolution of continuous variation processes, we go on to to explore in detail several real optimized control solutions, carried out in an industrial setting, providing the reader with a better understanding of the approaches developed.
Dumitru POPESCU, Amira GHARBI,
Dan STEFANOIU and Pierre BORNE
February 2017
List of Notations and Acronyms
Dynamic control model
Dynamic tracking model
AF-CLOE
Adaptively Filtered Closed Loop Output Error (identification method)
A,B,C,D
State-space representation of the continuous MIMO system
A
d
,B
d
,C
d
,D
d
State-space representation of the discrete MIMO system
A,b,c,d
State-space representation of the continuous SISO system
A
d
,b
d
,c
d
,d
d
State-space representation of the discrete SISO system
ARMAX
Model or class of identification models expressed by 3 terms: autoregressive (AR), moving average (MA) and exogenous control (X)
ARX
Identification model of autoregressive type (AR), with exogenous control (X)
(C,M)
Closed loop nominal system
(C,P)
Closed loop real system
DPRC
Differential Pressure Control System
FRC
Flow Control System
LRC
Level Control System
LS
Least Squares identification technique
RLS
Recursive Least Squares identification technique
PID
Proportional-integral-derivative algorithm
PRC
Pressure Control System
SM
State Model
TRC
Temperature Control System
BJ
Identification model of Box-Jenkins type
CL
Closed Loop (system, identification method etc.)
CLOE
Closed Loop Output Error (idenfication methods)
CLSI
Closed Loop System Identification
dB
decibel(s) – measuring unit for the signals/systems spectra
E-LSM
Extended Least Squares Method
F-CLOE
Filtered Closed Loop Output Error (identification method)
FIR
Finite Impulse Response (filter, system)
FT
Fourier Transform
G-CLOE
Generalized Closed Loop Output Error (identification method that replaces ARX model by BJ model)
G-LS
Generalized Least Squares (PEMM for the BJ model)
G(
s
)
Continuous system transfer function
G(
z
)
Discrete system transfer function
G
R
(
z
-1
), G
S
(
z
-1
)
Pre-specified polynomials for robust control
I-CLOE
Integral Closed Loop Output Error (identification method)I/O Input-Output (type of identification model, transformation, operator, etc.)
IIR
Infinite Impulse Response (filter, system)
I=f(V)
Photovoltaic Current-Voltage characteristic
L
Estimator matrix
LSM
Least Squares Method
M
Sylvester matrix
MIMO
Multi-Input Multi-Output (type of fully multi-variable model or system or process)
MISO
Multi-Input Single-Output (type of multi-variable model or system or process with several inputs and on single output)
MV-LSM
Multi-Variable Least Squares Method
OL
Open Loop (system, identification etc.)
OLOE
Open Loop Output Error (identification method)
OLSI
Open Loop System Identification
PEMM
Prediction Error Minimization Method (identification method)
P(
z
-1
)
Characteristic polynomial of the system
P=f(I,V)
Photovoltaic Power-Current, Voltage characterstic
PRS
Pseudo-Random signal
PV
Photovoltaic pannel
Q
Observability matrix
R
Controlability matrix
R-ELS
Recursive Extended Least Squares (identification method)
RST
Automatic regulator with 3 polynomials: R (regulation), S (sensitivity) and T (tracking)
RST-YK
RST regulator expressed in Youla-Kucera parametric form
SI
System identification
SISO
Single-Input Single-Output (type of model or system or process with one input and one output)
SNR
Signal-to-Noise Ratio
S
vy
(
j
ω)
Disturbance-output sensitivity function
W-CLOE
Weighted Closed Loop Output Error (identification method)
X-CLOE
Extended Closed Loop Output Error (identification method that replaces ARX model with ARMAX model)
X-OLOE
Extended Open Loop Output Error (identification method employed in case of ARMAX model instead of ARX model)
YK
Youla-Kucera (parametric expressions of a regulator)
|∆
M
(
j
ω)|
Modulus margin of the system robustness
