Scientia et Technica Año XXVI, Vol. 26, No. 04, diciembre de 2021. Universidad Tecnológica de Pereira. ISSN 0122-1701 y ISSN: 2344-7214
417
1
Abstract—Programmable Logic Controllers (PLC) are
an essential part of automated industrial production
processes since their first implementation, so
understanding the IEC 61131 standard and, above all,
section three defines the programming languages allowed
by PLCs take relevance over time. This work describes
each of the programming languages described in IEC
61131-3. Additionally, it implements an automation system
based on Structured Text with a Human Machine
Interface (HMI). The plant is a temperature process with a
classic control system developed using Matlab tools, such
as System Identification, PID Tuner, and Simulink. For
the HMI, was implemented the Codesys Group industrial
automation process platform. The Simulink PLC Coder
toolbox allows the strengthening of the connection between
the control system and the HMI. This program generates
the Structured Text of a control system developed in
Simulink. For the analysis of results, the control behavior
compared between Simulink and the system produced in
Codesys Group obtained an error of less than 0.34%.
Index Terms— Classic Control, IEC 61131-3, Simulink PLC
Coder, Structured Text, Programmable Logic Controllers.
Resumen— Los Controladores Lógicos Programables (PLC)
son una parte esencial de los procesos de producción industrial
automatizada desde su primera implementación, por lo que la
comprensión del estándar IEC 61131 y, sobre todo, la sección tres
que define los lenguajes de programación que permiten que los
PLC toman relevancia con el tiempo. Este trabajo describe cada
uno de los lenguajes de programación descritos en el estándar
IEC 61131-3. Además, implementa un sistema de automatización
This manuscript was sent on October 11, 2021 and accepted on November
30, 2021.
A. F. Barrera-Cuestas student of the Universidad Distrital Francisco José
de Caldas. Bogotá, Colombia. (e-mail: afbarrerac@correo.udistrital.edu.co).
M. G. Mantilla-Castañeda student of the Universidad Distrital Francisco
José de Caldas. Bogotá, Colombia. (e-mail:
mgmantillac@correo.udistrital.edu.co).
D. A. Giral-Ramírez professor of the Universidad Distrital Francisco José
de Caldas. Bogotá, Colombia. (e-mail: dagiralr@udistrital.edu.co).
O. D. Montoya-Giraldo professor of the Universidad Distrital Francisco
José de Caldas. Bogotá, Colombia. (e-mail: odmontoyag@udistrital.edu.co).
basado en texto estructurado con una interfaz hombre-
máquina (HMI). La planta es un proceso de temperatura con un
sistema de control clásico desarrollado con herramientas de
Matlab, como System Identification, PID Tuner y Simulink. Para
la HMI, se implementó la plataforma de procesos de
automatización industrial del Grupo Codesys. El toolbox
Simulink PLC Coder permite reforzar la conexión entre el
sistema de control y el HMI. Este programa genera el Texto
Estructurado de un sistema de control desarrollado en Simulink.
Para el análisis de resultados, el comportamiento del control
comparado entre Simulink y el sistema producido en Codesys
obtuvo un error inferior al 0,34%.
Palabras claves— Control Clásico, IEC 61131-3, Simulink
PLC Coder, Lenguaje Estructurado, Controladores Lógicos
Programables.
I. INTRODUCTION
OR diversification, technology control systems require the
incorporation of electronic, electrical, information, and
advanced manufacturing technologies in the means of
production for the automation and digitization of processes,
which is a requirement for new models of energy business and
an opportunity for government-academia (research) -industrial
technology synchrony in a sector that in its transition seeks to
integrate: renewable sources, direct current (DC) transport
systems, energy storage, distributed generation, measurement
systems, smart grids and the participation of end-users.
Elements, in their structure, are associated with new energy
generation and efficiency technologies, such as those targeted
by Industry 4.0, which has different technological trends.
Since their creation in the 60s, Programmable Logic
Controllers (PLC) have become indispensable devices when
carrying out an automation process and are relevant elements
in the challenges proposed by the industry. In the same way,
their constant updating, not only at the hardware level but at
the level of software and programming languages, allow these
instruments to keep at the forefront to the point of being seen
as an ideal option to face the fourth industrial revolution,
where, reduction of production times, optimization of the
levels of quality and resources used will allow us to enter into
this revolution; and, in turn, lead the industry to focus on
caring for the environment [1].
Temperature control using the simulink PLC
Coder and the IEC 61131 standard
Control de la temperatura mediante Simulink PLC Coder y el estándar IEC 61131
or
A. F. Barrera-Cuestas ; M. G. Mantilla-Castañeda ; D. A. Giral-Ramírez ;
O. D. Montoya-Giraldo .
DOI: https://doi.org/10.22517/23447214.24947
Artículo de investigación científica y tecnológica
F
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This article analyzes the IEC 61131-3 standard based on
each of the programming languages it describes, in addition to
implementing a controller through the use of industrial tools
such as Simulink PLC Coder and CODESYS. The objective is
to adopt a temperature control to an HMI through Structured
Text. The temperature control is carried out in Simulink,
employing the Toolbox Simulink PLC Coder that generates
the high-level programming structure. Finally, the HMI is
built and programmed in Codesys using the previously
obtained Structured Text.
II. STANDART IEC 61131-3
Section of the IEC 61131 standard created by the
International Electrotechnical Commission (IEC). This
standard defines the five programming languages for
Programmable Logic Controllers (PLC) to obtain order when
carrying out an industrial automation process through these
devices. The IEC standard determines the programming
languages: Ladder Diagram, Function Block Diagram (FBD),
Instruction List (IL), Structured Text (ST), and Sequential
Function Chart (SFC) [2].
Define abbreviations and acronyms the first time they are
used in the text, even after they have already been defined in
the abstract. Abbreviations such as IEEE, SI, ac, and dc do not
have to be defined. Abbreviations that incorporate periods
should not have spaces: write “C.N.R.S.,” not “C. N. R. S.”
Do not use abbreviations in the title unless they are
unavoidable (for example, “IEEE” in the title of this article).
A. Ladder Diagram
Designed primarily for processing Boolean signals using
power rail delimited graphical symbols that together resemble
steps in a ladder logic diagram. The Ladder programming
language allows, through the use of contact logic, to connect
elements in series (AND) or in parallel (OR), which contribute
to the flow or interruption of energy to supply an output called
coil [3].
B. Function Block Diagram (FBD)
Based on the standard, today retired, IEC 60617-12, the
FBD programming language defines a series of graphic
elements. These elements allow the generation of a structure
that from rectangular boxes with inputs, outputs, and flow
statements of control they manage to carry out logical,
arithmetic expressions or calls a function block so that once
interconnected employing signal flow lines they can feed the
outputs of the system [4].
C. Instruction List (IL)
The IL programming language, considered a low-level
programming language, consists of a progression of
instructions made up of modifiers and operations. These
instructions aim that when carried out, they recreate a list of
instructions. Even though its rapid processing makes one of its
main implementations that of control processes, the IEC has
decided not to take it into account for its next update [5].
D. Structured Text (ST)
The ST language is derived from the Pascal programming
language, and it is considered, by the standards, as one of the
high-level languages. The ST Language can control the
command flow by variables, expressions, or declarations of
type selection, iterations, complex mathematical formulas, or
calculations; it is easy for a programmer to apply and
understand [6] [7].
E. Sequential Function Chart (SFC)
The SFC was created to divide a more complex program
into small manageable units. SFC graphically describes the
control flow between systems using stages and transitions to
design sequential and parallel processes. The SFC language
allows the visualization of the dependencies or
interdependencies of the laps according to the process
location. It also releases programming its units in the
programming languages mentioned above [8] [9].
III. CONTROLLER WITH HMI
The proposed methodology was divided into six stages.
The flow charts in Fig. 1, Fig. 2 and Fig. 3 describe the six
stages.
A. Stage 1: Obtaining the transfer function in discrete time
It is necessary to establish the behavior of the system to be
analyzed to obtain the transfer function. Fig. 4 presents the
heating curve of the selected system; this curve was generated
by laboratory tests. The minimum temperature reached was
24.93 °C, and the maximum was 78.2 °C.
With the Matlab System Identification toolbox, the heating
curve of the thermal plant, and the supply voltage, the data is
processed to obtain the transfer function in the continuous-
time domain (1), gathering a 96.03% correspondence.
Employing the Matlab c2d command the transfer function is
obtained in the discrete-time domain (2), using the Tustin
discretization method. Fig. 4 represents the system heating
curve and the discrete-time transfer function curve.
()
32
2.72 0.05514
2.238 9.543 0.05266
S
s
H
s s s
+
=
+ + +
(1)
32
()
32
0.1523 0.1553 0.1461 0.1492
0.3798 0.1075 0.5011
z
z z z
H
z z z
+ − −
=
+ + +
(2)
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Fig. 1. Stage 1, 2 and 3 of the design and implementation of the controller
with HMI. Own Work
Fig. 2. Stage 4 of the design and implementation of the controller with HMI.
Own Work
Fig. 3. Stage 5 and 6 of the design and implementation of the controller with
HMI. Own Work
Stage 1:
Discrete time
transfer function
Stage 2:
PID
controller parameters
Stage 3:
Plant and
control simulation
End stage 1
End stage 2
End stage 3
Plant characterization PID Tuner
Simulink
Load transfer
function
Implement plant and
control
Set control, speed
and aggressiveness
Check plant and
generate disturbances
cation
toolbox
No
No
Yes
Yes
Yes
No
Transfer function
model
Get Kp, Ki and Kd
Get Kp, Ki and Kd
Check behavior
cient behavior?
correspondence
< 90%
Correct operation?
Stage 1:
Discrete time
transfer
function
Stage 4:
Simulink PLC
Coder
End stage 4
Generate subsystem
of the architecture
PLC Coder
Check plant and
generate disturbances
Run PLC coder
compiler
Path to save the
generated code
Set up automation
software
Stage 5:
PLC coder
environment Codesys
Stage 6:
PLC coder
environment Codesys
End stage 6
End stage 5
Run Codesys
Output parameters
to compare Codesys
and Simulink
Create SFC project
Import the code from
PLC coder
1
1
Perform adjustment
Yes
No
Correct operation?
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Fig. 4. Plant heating curve and discrete transfer function. Own Work
B. Stage 2: Obtaining the constants of the classical controller
Using the PID Tuner toolbox on the transfer function in the
discrete-time domain, a PI controller is implemented that
adjusts to the system under study. A fast response time and a
robust transient behavior are selected, obtaining the constants
of proportionality and integrity described in Table I and an
output behavior as observed in Fig. 5.
TABLE I
CLASSIC CONTROLLER CONSTANTS
Constant
Values
Constant of proportionality (Kp)
0.49989
Integrity constants (Ki)
0.45537
Fig. 5. The behavior of the plant with a PI controller. Own Work
C. Stage 3: Design and simulation of the plant architecture
in the Simulink environment
Using the Simulink block diagram environment, the system
behavior is simulated, implementing the PI controller, as seen
in Fig. 6 and Fig. 7 shows the behavior of the system with and
without control when applying disruptions.
Fig. 6. System modeling in Simulink. Own Work
Fig. 7. Plant with PI control and without PI control with two disruptions.
Own Work
D. Stage 4: Installation and configuration of the Simulink
PLC Coder toolbox
The Matlab toolbox, Simulink PLC Coder, is a tool that
allows the generation of programming code in ST language
from Simulink models. It is possible to generate the
programming code in 15 different simulators, including
Codesys 3.5 [10]. Fig. 8 presents the programming code
obtained through Simulink PLC Coder for the controller
described in Fig. 6.
Fig. 8. Programming code generated by Simulink PLC Coder. Own Work
E. Stage 5: Adaptation of the code generated in the Codesys
environment
Some modifications are required to implement the
programming code generated by Simulink PLC Coder in the
Codesys interface; adjustments such as, change the global
variables of constant type for general global variables to
modify their state once the project is carried out. Additionally,
in ST is created a statement that represents the feedback of the
system of Fig. 6. The Simulink PLC Coder toolbox does not
have the support to generate the closed-loop so that it is
necessary to make that adjustment. Fig. 9 presents the
programming code obtained with closed-loop.
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Fig. 9. Programming code with feedback. Own Work
F. Stage 6: Comparison of the Simulink and Codesys output
curves
To determine qualitatively and quantitatively the graphs
obtained in Simulink and Codesys, it was necessary to carry
out three case studies to find the percentage of error between
them. So, scenarios are analyzed where the behavior of each
of the graphs is compared at specific points. These scenarios
are:
1) Initial maximum temperature: The maximum temperature
that the plant reaches during the heating process at the initial
setpoint.
2) Initial maximum temperature: The maximum temperature
that the plant reaches during the heating process at the initial
setpoint.
3) Maximum temperature in the disruption: The maximum
temperature that the plant reaches when it is applied disruption
to the system.
4) The maximum temperature change of the setpoint: The
maximum temperature that the plant reaches when
establishing a new value of the setpoint.
5) Stabilization: Temperature registered by the plant when
stabilizing at the second setpoint.
6) Before the disturbance: The indicated temperature by the
plant before the disruption occurs.
7) Before the setpoint changes: The recorded temperature by
the plant before the setpoint change occurs.
From (3) is found the percentage of error between behaviors.
%
Simulink CODESYS
Simulink
TT
e
T
−
=
(3)
Where:
T
Simulink
: Temperature recorded by Simulink
T
Codesys
: Temperature recorded by Codesys.
Case 1: Disruption and positive setpoint
The behavior and results obtained in Fig. 10 and Table II
relate the curves produced by Simulink and Codesys from
assigning a negative setpoint and disturbance. These results
are from the following parameters:
▪ From the beginning of the code implementation, it
establishes an initial setpoint of 30°C.
▪ Disruption generation of 20 °C within 120 seconds of
executing the code.
▪ Updating the setpoint to 60 °C within 200 seconds of
carrying out the code.
Fig. 10. The heating curve obtained from the Codesys (blue) and Simulink
(green) software for positive disruption and setpoint. Own Work
TABLE II
ERRORS BETWEEN SIMULINK AND CODESYS FOR HEATING CURVE TIMES
DETERMINED WITH POSITIVE DISTURBANCE AND SETPOINT
Action of interest
Time [s]
T. Codesys
[°C]
T. Simulink
[°C]
Error [%]
Max. initial Temp.
41
31.4846
31.390
0.3010
Max. disruption
Temp.
120
50.4341
49.0293
2.8652
Max change setpoint
Temp.
241
61.1377
61.1775
0.1281
Stabilization
379
60.1377
60.1431
0.0089
Before the
disruption
119
30.4415
30.4518
0.3382
Before the setpoint
change
199
29.5427
29.5249
0.0602
Case 2: Disturbance and negative setpoint
The behavior and results obtained in Fig. 11 and Table III
relate the curves produced by Simulink and Codesys, the error
percentages obtained in each of them, from assigning a
negative setpoint and disruption. These results are from the
following parameters:
▪ From the beginning of the code completion, it establishes
an initial setpoint of 50°C.
▪ Generation of a disruption of -20 °C within 120 seconds
of executing the code.
▪ Updating the setpoint to 30 °C within 200 seconds of
implementing the code.
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Fig. 11. The heating curve obtained from the Codesys (blue) and Simulink
(green) software for a negative disruption and setpoint. Own Work
TABLE III
ERRORS BETWEEN SIMULINK AND CODESYS FOR DETERMINED HEATING
CURVE TIMES WITH NEGATIVE DISTURBANCE AND SETPOINT
Action of interest
Time
[s]
T. Codesys
[°C]
T. Simulink
[°C]
Error [%]
Max. initial Temp.
43
52.4471
52.3377
0.2090
Max. disruption
Temp.
120
30.5275
32.1553
5.0623
Max change
setpoint Temp.
248
29.4014
29.4275
0.08869
Stabilization
379
29.9299
29.9271
0.00935
Before the
disruption
119
50.7358
50.753
0.03388
Before the setpoint
change
199
50.7685
50.785
0.03248
Case 3: Positive disturbance and negative setpoint
The behavior and results recorded in Fig. 12 and Table IV
relate the curves produced by Simulink and Codesys and the
error percentages obtained in each of them from assigning a
positive disturbance and a negative setpoint. These results are
from the following parameters:
▪ From the beginning of the code execution, it sets down
an initial setpoint of 40°C.
▪ Generation of a 30 °C disruption within 120 seconds
after having implemented the code.
▪ Updating the setpoint to 28 °C within 200 seconds of
having executed the code.
Fig. 12. The heating curve obtained from the Codesys (blue) and Simulink
(green) software for positive disruption and negative setpoint. Own work
TABLE IV
ERRORS BETWEEN SIMULINK AND CODESYS FOR HEATING CURVE TIMES
DETERMINED WITH POSITIVE DISTURBANCE AND NEGATIVE SETPOINT
Action of interest
Time
[s]
T. Codesys
[°C]
T. Simulink
[°C]
Error
[%]
Max. initial Temp.
44
41.9431
41.8701
0.1743
Max. disruption
Temp.
120
70.5788
68.4699
3.0800
Max change
setpoint Temp.
237
27.0289
27.0732
0.1636
Stabilization
379
27.9021
27.9000
0.0075
Before the
disruption
119
40.5887
40.6024
0.0034
Before the setpoint
change
119
39.2835
39.268
0.0395
G. Human-Machine Interface (HMI)
Employing the visualization manager of the Codesys
software is possible to present the most relevant information
on the plant control process. This visualization manager
allows the user to observe the current status of the thermal
plant; it also has the freedom to assign a new setpoint and
disruption value if required. In that sense, three screens are
designed to have total control of the plant. These screens are
the presentation screen, control-display screen, and control-
management screen.
1) Presentation screen: The presentation screen defines the
name and title of the project, the names of the developers, and
the institutional affiliation (Fig. 13).
Fig. 13. HMI splash screen layout. Own Work
2) Control-display screen: In the control and visualization
screen, the user can identify the current behavior of the
thermal plant and the disturbances that have been generated,
comparing the setpoint established with the current
temperature of the plant. In the same way, it displays the on or
off status of the plant, the current temperature value inside it,
and the error, in Celsius degrees. Finally, it has the option of
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establishing a new setpoint value and generating disruptions in
the system; it also allows turning the plant on or off (Fig. 14).
Fig. 14. Design of the HMI presentation and control screen. Own Work
3) Control-management screen: The control and
management screen allows determining, from the activation of
the indicators and a bar-shaped temperature chart, the current
status of the plant from the temperature registered inside it. So
that the thermal scale is divided into five ranges: very high,
high, medium, low, or very low. Using two additional
indicators, the user could determine if the system is stable or if
a setpoint has been established outside the allowed range; and
identify which does not guarantee the correct operation of the
control (Fig. 15).
Finally, the user can set a new setpoint value and display
the error of the current work.
Fig. 15. Design of the HMI control and management screen. Own Work
IV. GENERATED MATERIAL
Table V shows the name and the corresponding links to the
videos that complement the explanation of the stages
implemented.
TABLE V
ERRORS BETWEEN SIMULINK AND CODESYS FOR HEATING CURVE TIMES
DETERMINED WITH POSITIVE DISTURBANCE AND NEGATIVE SETPOINT
Stage
Link
1
https://youtu.be/T6r58dViQyY
2
https://youtu.be/f072iUgtzAo
3
https://youtu.be/6WTzW24Uufk
4
https://youtu.be/WOqBgep14i0
5
https://youtu.be/giOsijFyxpM
6
https://youtu.be/m3Bhq1Ikpio
V. CONCLUSION
From the programming languages presented in the IEC
61131-3 standard, it is possible to highlight the facilities that
come with developing a project in high-level programming
languages (ST and SFC). The ST has selection type and
iteration type structures; the similarity between the
programming syntax with languages such as C ++, Matlab or
Python, makes the adaptability for a programmer who does
not know the standard more enjoyable. The SFC simplifies
projects where sequential, combinational, and parallel systems
predominate.
Analyzing the tables, It determinates that the error of the
results obtained from the simulated system in Codesys and the
system simulated in Simulink is less than 0.34% in all its
measurements, except for the one generated in the peak of
maximum or minimum disruption.
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