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Series Editor
Bernard Dubuisson

Process Control Design for Industrial Applications

Dumitru Popescu

Amira Gharbi

Dan Stefanoiu

Pierre Borne

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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,


February 2017

List of Notations and Acronyms

images Dynamic control model
images 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
Ad,Bd,Cd,Dd State-space representation of the discrete MIMO system
A,b,c,d State-space representation of the continuous SISO system
Ad,bd,cd,dd 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
GR(z-1), GS(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
Svy(jω) 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 (jω)| Modulus margin of the system robustness