Indian Journal of Science and Technology, Vol 8(35), DOI: 10.17485/ijst/2015/v8i35/82262, December 2015
ISSN (Print) : 0974-6846
ISSN (Online) : 0974-5645
Concept and Application of PID Control and
Implementation of Continuous PID
Controller in Siemens PLCs
Babak Rooholahi* and P. Lokender Reddy
Department of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad - 500007,
Telangana, India; babakroohollahi@gmail.com; lokenderreddyp@gmail.com
Abstract
Background/Objectives: The main objectives are to define the concept and application of PID control in parallel structure
of PID controller and to describe implementation of the continuous PID controller in SIMATIC STEP 7. Methods/Statistical
Analysis: Various designs of PID controllers in SIMATIC STEP 7 are available for different kinds of feedback control systems. Parallel structure is used in the algorithm of PID controllers in Siemens PLCs. In this study PID controller with the
continuous control function block FB41 (CONT_C) is analyzed. Programming with PID controller in Siemens PLCs with
continuous Manipulated Variable output and the option of influencing the Manipulated Value manually is thoroughly discussed. Findings: PID controller is the most common form of feedback control and it can be found in all the areas where
control loops exist. The simplicity of understanding of the PID algorithm is one of its advantages but due to a wide range of
systems and different manufacturers of control systems with various design and tuning methods of PID controllers in their
products, the implementation process gets complicated. This difficulty is reduced by using several kinds of PID controllers
for different types of systems in Siemens control systems. FB41 is a function block of continuous PID controller which was
designed for Siemens PLCs to provide control performance with analog signals; it can function both in a manual and automatic mode.The most important part of FB41 functioning is addressing of the analog Input and Output in this function
block. The results of P, I, D, PI, PD and PID controller implementation in FB41 have been compared. Applications/
Improvements: This continuous control function block can be used in different kinds of control loop systems where ­analog
input is sensed and analog output is required.
Keywords: Analog Signal, Continuous PID Controller, Control System, FB41 (CONT_C), Feedback Control, Siemens PLCs
1. Introduction
A control system is a combination of components which
senses, manages and regulates the behavior of another
system to produce the desired output. Automation is
the process where different methods or control systems
are used to manage a process. In industrial control,
Programmable Logic Controller (PLC) has the extremely
wide range of applications. PLC and PLC based controllers are the most important and useful control systems.
Since these successful and beneficial control systems
are produced, the PID control methodology has been
extensively studied by researchers and well understood
*Author for correspondence
by practitioners. Most of PID controllers have been
­developed on the state-space model with this assumption
that all state variables are measurable or on the inputoutput model for a linear system1. PID controllers have
been widely used in many industrial processes because
they have only three control parameters and we can easily
understand their physical meanings. There are many classical techniques for designing and tuning PID controller
parameters (KP, KI, KD) which can be easily understood
and applied2,3. By adjusting these three gain values, the
settling time, overshoot and rise time of the system can
be controlled in order to obtain a desired system output.
Even though many control systems using PID control have
Concept and Application of PID Control and Implementation of Continuous PID Controller in Siemens PLCs
proved satisfactory, it still has a wide range of ­applications
in industrial control4.
PID controllers in Siemens PLCs are exclusively
­professional. They are distinguished by their classification for using in different kinds of control systems. In
step 7 Siemens PLC programming software different
function blocks of PID are designed for various kinds of
systems. These function blocks are compatible with their
own control systems. In the following parts of this paper
continuous PID control with FB41 (CONT_C) function block, its implementation and performance will be
­discussed.
2. Control System
Control systems are used to minimize the consequences
of process variations and environmental influences on
the quality of process control. Sometimes these variations
and influences are so significant that the conventional linear controllers with constant parameters fail to perform
successfully. The primary functions of controllers are controlling equipment or machines and maintaining stability
of their performances5. Among different brands of control systems, Siemens control systems being professional
have an essential role in this industry. There are different kinds of industrial control systems and one of them is
Programmable Logic Controller (PLC).
Improvements of microelectronics and application of
mathematical methods in control theory contributed to
control systems development. One of these modern control techniques and mathematical based control methods
is Advanced Process Control (APC) technique which is
used in control systems and offered by all the PLC manufacturers.To increase the process capacity, efficiency,
quality of products and costs savings in APC technique different kinds of tools for self-tuning, manufacturing costs
and energy savings optimizationare designed. While some
of the APC techniques are installed directly in the PLC,
most of them are implemented as supervisory functions on
higher level of the control system6. Due to the usage of APC
and microelectronics in PLCs many components, which
were applied in relay logic systems are out of use now and
are replaced by function block diagrams in PLCs.
in a process produce the output. In closed loop control
system which is also called a feedback control system
the output is analyzed and corrected based on feedback.
Feedback is a process in which the past information influences the present or future. In a feedback control system
information about performance is measured by sensors.
The comparison of this information with setpoint is used
in control algorithms to control the actuators and correct
the performance of the system.
3. PID Control
PID controllers which are applied in around 90% of
industrial loops are the most often used controllers. It is
beneficial because of their simplicity, robust nature, ease
of implementation and design and less number of tuning
parameters7,8. P, I and D are three parameters which have
to be tuned manually or by some tuning techniques to
design a PID controller. The proportional value defines
the reaction of the current error, the integral value defines
the reaction based on the sum of recent errors and the
derivative value defines the reaction based on the rate at
which the error has been changing the weighted sum of
these three actions that is used to adjust the process via
the final control element9.
Due to successful implementation, wide accessibility
and use of intelligibility PID control became an essential
tool to control difficult processes. PID controller controls the process successfully even with the increased
­complexity10.
These values have been adjusted in order to achieve a
desired constant output from the system. The PID controller algorithm that operates as a position algorithm is
shown in Figure 1.
The PID algorithm can function under three basic
modes, the Proportional mode, Integral mode and the
Derivative mode. It is necessary to determine which
2.1 Feedback Control System
Control systems are classified into two main types: open
loop and closed loop. In open loop control system inputs
2
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Figure 1. PID controller
Indian Journal of Science and Technology
Babak Rooholahi and P. Lokender Reddy
controller has to be used for the process, before ­applying
the algorithm in the system11. Then the parameters or
settings which are used for each mode can be finalized11. Sometimes only one or two parameters should be
used to obtain the proper system control which can be
achieved by deactivating the other parameters (setting
them to zero); in these cases it will be a P, I, PI, PD and
PID ­controller.
The Process Variable (PV) is measured by the sensors
from the field; the desired value is called SetPoint (SP)
and determined manually set by the operator; the input to
the process and the output of the PID controller are called
Manipulated Variable (MV), Control Variable (CV) or
control signal u (t); the difference between the Process
Variable (PV) and the SetPoint (SP) is the error (e(t)) that
is continuously calculated by a PID controller.
PID controller by adjusting the error itself, integral
of the error and the derivative of the error as Control
Variable (CV) tries to minimize the error.
3.1 Standard Structures of PID Controllers
Parallel structure, three-term functionality and a typical
structure of a PID control system are shown in Figure 2. A
mathematical description of the PID controller is shown
in the following Equations (1, 2):
∫
(1)
or
t
u (t ) = K P e (t ) + K I e (t ) dt + K D
de (t )
∫
0
dt
,
• Rise time – the time it takes for the Process Variable
(PV) to rise above 90% of the SetPoint (SP) for the first
time,
• Overshoot – the maximum swing above the SetPoint
(SP),
• Settling time – the time it takes for the Process Variable
(PV) to settle to its steady state,
• Steady-state error – the difference between the
­steady-state variable and the SetPoint (SP).
By tuning the PID gain values, these characteristics
can be controlled and optimized to achieve a desired
­system output.
The Laplace transfer function of a PID controller is
expressed in the Equation 3:
GPID ( s ) =
U (s )


1
= K P 1 +
+ TD s  , E (s )

 TI s
(3)
Where U(s) is the control signal, E(s) is the error signal, s
is the variable of Laplace transfer function, KP is the proportional gain, TI and TD are the integral and derivative
time constants respectively.
The PID control signal is a sum of three terms 4:
U ( s ) = K P E ( s ) + K I 1 E ( s ) + K D sE ( s ) = U P ( s ) + U I ( s ) + U D ( s ) ,(4)
s
t

de (t ) 
1
,
u (t ) = K P  e ( t ) +
e (t ) dt + TD
dt  TI

0

Four main features of the closed-loop step response are:
(2)
where u(t) is the control signal and e(t) is the error signal.
The reference value is called the SetPoint. The difference
between the Process Variable (PV) and the SetPoint (SP)
is the error signal e(t) : e(t) = PV– SP4. , Kp, KI and KD are
the proportional gain, the integral gain and the derivative
gain respectively.
Where K I = K P / TI (5) is the integral gain and
K D = K PTD (6) is the d
­ erivative gain.
The three-term functionalities include:
3.1.1 Proportional Term
The proportional term provide an overall control action
proportional to the error signal through the all pass gain
factor. Increasing the proportional gain is the cause of
decreasing the error and increasing the oscillation of
the system. Tuning theory and practical implementation
show that the proportional term should influence the
output.
3.1.2 Integral Term
Figure 2. A typical PID control structure.
Vol 8(35) December 2015 | www.indjst.org
The integral term reduces steady-state errors through
­low-frequency compensation. Integral term is proportional
to the integral of the error. As it integrates the error over
the time, it can cause the overshoot of present value to the
SetPoint value.
Indian Journal of Science and Technology
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Concept and Application of PID Control and Implementation of Continuous PID Controller in Siemens PLCs
3.1.3 Derivative Term
The derivative term improves transient response through
high-frequency compensation. The rate of error changes
is the contribution of the derivative term. It gives an additional control by predicting errors and future behavior of
the system.
Due to its variable impact on system stability, ­derivative
action is rarely used in practice. The pure derivative action
is never used because of the derivative kick produced in
the control signal for a step input and to the undesirable
noise amplification. It is usually replaced by a first-order
low pass filter4,12.
3.2 Design and Tuning of PID Controller
Designing and tuning of a PID controller is not easy when
stability and short transient of the system are desirable.
Repeated changes of initial designs through computer
simulation need to be done to achieve a desired performance of the system. Control parameters (KP, KI or TI,
KD, or TD) must be tuned jointly to the optimum values
for the desired control response. Although there are individual effects of these three parameters on the closed loop
performance of stable plants which are summarized in
Table 1. For example, while KI and KD are fixed, increasing KP alone can rise time, increase overshoot, slightly
increase settling time, decrease the steady-state error and
decrease stability ­margins12.
For manual tuning in online system one of the ­methods
is first setting all gains to zero. Increase the KP value until
the constant oscillation of the output is obtained, then the
KP should be set to approximately half of that value for a
quarter amplitude decay type response. After that increase
KI to minimize the P term offset in a particular time for
the process considering that too much KI can be a cause of
instability. Finally, increase KD, if required, until the system becomes acceptably quick to reach the SetPoint and
4
Rise
Time
Overshoot
Settling
Steady-
Time
State Error
Stability
4.1 Analog Signals
Increasing
Decrease
KP
Increase
Small
Increase
Decrease
Degrade
Increasing Small
KI
Decrease
Increase
Increase
Large
Decrease
Degrade
Increasing Small
KD
Decrease
Decrease
Decrease
Minor
Change
Improve
Vol 8(35) December 2015 | www.indjst.org
4. PID Control in Siemens PLCs
The Function Blocks (FBs) of the PID control ­package
consist of controller blocks for Continuous Control FB41
(CONT_C), for step control FB42 (CONT_S), for Pulse
Duration Modulation FB43 (PULSEGEN), for continuous temperature control FB58 (TCONT_CP) and for
temperature step control FB59 (TCONT_S). A controller
created with the FBs consists of a series of sub functions
that you can activate or deactivate. Apart from the actual
controller with its PID algorithm, integrated functions
can also be used for processing the setpoint, process variable and adapting the calculated manipulated variable.
Both slow processes (temperatures, tank levels etc.) and
very fast processes (flow rate, motor speed etc.) can be
controlled without any restrictionin terms of the type of
process. Good control quality can be achieved only if the
controller type suits your situation and adapts it to the
time response of the process13.
Table 1. Effects of independent P, I and D tuning on
closed-loop response.
Parameter
decrease the oscillation. However, too much KD will lead
to overshoot in the system. A quick PID controller loop
normally has a little overshoots to achieve the desired
value very fast; but in some systems cannot receive overshoot. In these systems a KP should be set remarkably less
than half of the KP setting which provoked oscillation, to
make over-damped in the closed-loop system.
Design and tuning of the controller and selecting of its
static (P component) and dynamic (I and D component)
parameters are dependent on the static behavior (gain)
and the dynamic characteristics (time lag, dead time, reset
time etc.) of the process13. Different methods of PID controller design and tuning are applied for ­different types of
continuous systems.
The modified auto-tuning PID controller was
­implemented in a SIEMENS PLC, product family
SIMATIC S7-300 and S7-400. FB50 is a PID Self Tuner in
Step 7 TunPID Library by Siemens which can be used for
tuning parameters of PI/PID regulators in function blocks
FB41, FB42, software packages Standard PID Control,
Modular PID control and function modules FM 355C6.
Continuous signals have an infinite number of states. These
variable signals received from different filed devices (sensors) need to be converted to digital signals by an Analog
to Digital converter (A/D) before CPU ­processing. This
Indian Journal of Science and Technology
Babak Rooholahi and P. Lokender Reddy
digitization is performed inside an analog input interface within the Input/Output (I/O) module. The analog
device’s current output is often transformed to a voltage
by a built‐in resistor. A common standard used in representing analog signals is when 4mA corresponds to
the minimum signal level (1V) and 20mA the maximum
(5V). Digitized values corresponding to the continuous
values between 1‐5V are rated by the PLC’s CPU, values
ranging above or below are discarded by the CPU. A value
< 0,296V is used for detecting cable failure.
Table 2 shows Siemens STEP7 format14,15.
The CPU makes decisions and executes control
instructions based on program instructions in memory
to determine what the PLC will do for a specific input.
Control instructions from the CPU are converted into
a Digital or Analog signals by output modules to control
different field devices (actuators). This data transformation is performed by a Digital‐to‐Analog (D/A) converter.
It is the exact opposite of the transformation in an analog input interface. An analog output interface within
the I/O module is used to address devices controlled by a
­continuous voltage or current.
4.2 Continuous PID Control with FB41
“CONT_C”
FB41 (CONT_C) is used to implement the PID ­controller
with continuous analog input and output on SIMATIC
Table 2. SIMATIC STEP7 format, measuring range
1‐5V.
Measuring
range 1-5 V
Decimal (value seen
from the CPU Point
of view)
Range
>5,704
32767
Overflow
5,704
.
.
5,00014
32511
.
.
27649
Over range
5,000
4,000
.
.
1,000
27648
20736
.
.
0
Rated range
(normal
operation range)
0,99986
.
.
0,296
-1
.
.
-4864
Undershoot
range
<0,296
-32768
Underflow
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STEP7. By activating or deactivating the PID parameters
and process variable and adjusting the proper value for it,
the desired PID controller for different kinds of systems
can be set. Apart from this capability, there is an option for
FB41 to provide continuous manipulated variable output in
PID controller process ormanually forcing the ­manipulated
value by the operator13,16. The FB41 (CONT_C) block
­diagram, input default mode, number, name and description of each parameter are shown in Figure 3.
4.2.1 Process Variable Section
The Process Variable can be given in PV_IN manually to
the PID controller when PVPER_ON is in default mode
(deactivated). If PVPER_ON is set, the Process Variable
must be read from the PV_PER which is connected to
peripheral I/O. The choice between Process Variable IN
(PV_IN) and Process Variable Peripheral (PV_PER) is
determined by PVPER_ON. The function which converts
the PV_PER to the floating-point format of -100 to +100
% is CRP_IN and it has calculated by 7:
Output of CRP_IN = PV_PER ∗ 100 27648
(7)
The output of CRP_IN is normalizedby the following
formula ):
Output of PV_NORM = (output of CRP_IN) * PV_FAC
+ PV_OFF
(8)
PV_FAC and PV_OFF are initiallyset to 1 and 0
respectively.
4.2. Manipulated Value Section
It is possible to switch over between Manual and Automatic
Mode (MAN and Manipulated Value (LMN)) by setting/
resetting the Manual Value On (MAN_ON) (if it is set, the
manual value is set as the output in manipulated value).
In the manual mode, the Manipulated Variable (MV) is
adjusted to a selected value manually. It means there are no
any sudden changes in the Manipulated Value due to a switchover from the manual mode to the automatic mode.
The Manipulated Value is always limited to an upper
and lower limit (LMN_HLM and LMN_LLM).
“High Limit of Manipulated Value Reached” or “Low
Limit of Manipulated Value Reached” (QLMN_HLM and
QLMN_LLM) indicate when the upper or lower limit of
the Manipulated Value (LMN_HLM and LMN_LLM) has
been exceeded.
Indian Journal of Science and Technology
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Concept and Application of PID Control and Implementation of Continuous PID Controller in Siemens PLCs
Figure 3. Continuous PID control block FB41 (CONT_C).
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Indian Journal of Science and Technology
Babak Rooholahi and P. Lokender Reddy
LMNLIMIT can be normalized by LMN_NORM
function according to the following formula 9:
LMN = (output of LMNLIMIT) *LMN_FAC +
LMN_OFF
(9)
LMN_FAC and LMN_OFF have the default 1 and 0
respectively.
LMN_PER is the manipulated value in the peripheral
format. It is converted from the floating-point value format (LMN) to the peripheral value format (LMN_PER)
by the CRP_OUT function 10:
LMN_PER = LMN ∗
27648
100
(10)
4.3 Inputting/Outputting Analog Values in
STEP 7
Integer and Real types of data have an important function
in process analog signals for SIMATIC S7. Analog inputs
are received as real numbers in the format INT for analog
values processing. Because of rounding variables by INT,
for an accurate further processing the REAL numbers are
considered. Analog values are inputted as WORD with the
integer (INT) data type format in the PLC. The address
of these WORD values are accessible with the ­following
instruction17:
L PIW x
T PQW x
for ‘Load analog Input Word’
for ‘Transfer analog Output Word’
The addressing of the input/output WORDs depends
on the start address of the module. For the module
installed in slot 4 the default start address will be 256. For
further analog modules default start address of each slot
will increase by 16. This address can be checked in hardware configuration table for each slot. The address of the
first analog input on slot 6 is PIW 288, the second analog
input is PIW 290 and the first analog output is PQW 288
etc.17.
4.4 Case Study and Experimental Results
The system consisting of PLC SIMATIC300 with CPU
315-2 PN/DP, digital inputs module DI SM321, digital outputs module DO SM322 and analog Input-Output modules
AI/AO SM334 is used to evaluate PID controller in this
study. STEP7 is the Siemens PLCs programming software;
the Ethernet connection is the connection type between
PC and PLC which is provided by this PLC model.
Vol 8(35) December 2015 | www.indjst.org
The first step of the programming in STEP7 is
Hardware Configuration (HW Config). It is required to
determine the hardware which consists of power supply,
PLC CPU model number, all the modules used in the
control system and the network connection type between
PC and PLC in HW Config.
The programming will start by opening the first
Operation Block (OB1) in the SIMATIC Manager window.
The PID block FB41 CONT_C can be found in the program
“elements libraries”. It is necessary to determine the proper
addresses or correct values of 26 inputs and 9 ­outputs in
PID block FB41 to implement it in the program.
Addressing for each parameter of the block should
include digital input, digital output, Memory Double
word (MD) or Periphery Input and Output Word (PIW/
PQW) addresses. VAT (Variable Access Table) should be
made to monitor the word format parameters and to put
the addresses with their values, modify them in the RUN
mode and specify the values of Display format. VAT can
specify the address of each value and display the Status
Value and Modify Value as it is shown in Figure 4.
Figure 5 shows the performance of P controller in
online mode where PV_PER is received from the field
and PVPER_ON is set. Here. kp=1.
Implementation of PID controlleris done by ­activating
the P, I and D parameters. Figure 6 shows PID controller
performance in offline mode where PVPER_ON is FALSE,
MAN_ON is TRUE and PV_IN has the value of 20.
Comparing the output of P, I, D, PI, PD and PID
controllers and analysis of their response speed to the
variation of process variable show that P controller has
the minimum speed and very slow with the specific ­offset
and I, PI, PD, PID and D controllers respectively have the
minimum to maximum speed of response to reach the
maximum manipulated value: D > PID > PD > PI > I > P.
Usually Proportional Integral (PI) controllers required
to meeting the optimal response in linear systems and
PID controllers are required to meet desired response in
nonlinear systems18.
5. Discussion and Conclusions
PID control remains a widely applicable and significant
control technique due to successful implementation, simplicity in use and understanding. Combination of these
reasons explain why PID control cannot be replaced by
another higher order controllers. PID control advantages
and successful results can be obtained by ­understanding
Indian Journal of Science and Technology
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Concept and Application of PID Control and Implementation of Continuous PID Controller in Siemens PLCs
Figure 4. VAT (Variable Access Table).
Figure 5. Performance of P controller in online mode.
Figure 6. PID controller performance in offline.
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Indian Journal of Science and Technology
Babak Rooholahi and P. Lokender Reddy
the nature of a particular system and choosing the proper
design and tuning of the PID controller. Tuning and
design of PID control, which depend on the nature of each
system, will vary according to the system characteristics.
PID controllers in Siemens PLCs are very efficient and
widely used in industry. They are also common in complicated and professional systems. PID controller in Siemens
PLCs has different function blocks such as continuous
control FB41 (CONT_C), step control FB42 (CONT_S),
continuous temperature control FB58 (TCONT_CP) and
temperature step control FB59 (TCONT_S) to control
different kinds of systems.
In this paper PID function block with continuous
control FB41 (CONT_C) has been discussed as the major
concept of PID controller function block in Siemens
PLCs which can be used for all kinds of systems by implementing the correct design and tune. To implement FB41
specifying the proper addressing and correct values of 26
inputs and 9 outputs is essential. As the result of applying the standard structure of PID controller in Siemens
PLCs by FB41 in online and offline mode, the speed of
response to reach the maximum manipulated value has
been studied.
6. References
1.Cho J, Kwag DG, Kim BS. Control of chaotic resonance
phenomena using prototypes in manifold forms. Indian
Journal of Science and Technology. 2015 Oct; 8(26):1–7.
2.Sabir MM, Khan JA. Optimal design of PID controller
for the speed control of DC motor by using metaheuristic
techniques. Advances in Artificial Neural Systems. 2014,
126317.
3.Namba R, Yamamoto T, Kaneda M. Robust PID controller and its application. Proceedings of the 1997 IEEE
International Conference on Systems, Man and Cybernetics,
Computational Cybernetics and Simulation; Orlando FL.
1997. p. 3636–41.
4.Xue D, Chen YQ, Atherton DP. Linear feedback control
analysis and design with MATLAB. London, UK: SpringerVerlag; 2002.
5.Peric N, Branica I, Petrovic I. Modification and application
of autotuning PID controller. Proceedings of the 8th IEEE
Vol 8(35) December 2015 | www.indjst.org
Mediterranean Conference on Control and Automation
(MED 2000); Rio Patras, Greece. 2000 Jan.
6.Kocian J, Koziorek J. Self tuning techniques on PLC
­background and control systems with self tuning methods
design. Advances in Electrical and Electronic Engineering.
2010 Jun; 8(2):40– 7.
7.Er MJ, Lei Y. Hybrid fuzzy proportional – integral plus
conventional derivative control of linear and nonlinear
systems. IEEE Transactions on Industrial Electronics. 2002
Jan; 48(6):1109–17.
8.Lakshmi Narayana K, Naveen Kumar V, Dhivya M, Prejila
Raj R. Application of ant colony optimization in tuning a
PID controller to a conical tank. Indian Journal of Science
and Technology. 2015 Jan; 8(S2):217–23.
9.Sethuramalingam TK, Nagaraj B. PID controller tuning using soft computing methodologies for industrial
process - A comparative approach. Indian Journal of Science
and Technology. 2014; 7(S7):140–5.
10.Meena AR, Senthil Kumar S. Design of GA tuned
two-degree freedom of PID controller for an interconnected three area automatic generation control system.
Indian Journal of Science and Technology. 2015 Jun;
8(12):1–10.
11.Pamela D, Premi MSG. TCP/IP based control and automation of temperature process. Indian Journal of Science and
Technology. 2015 Jun; 8(11):1–6.
12.Li Y, Ang KH, Chong GCY. PID control system analysis
and design. IEEE Control Systems Magazine. 2006 Feb;
26(1):32–41.
13.Siemens, SIMATIC, Standard PID Control; 2003. p. 1–256.
14.Al Hadawi E. Development of a Continuous Blending
System (Controlling a blending system). Division of
Industrial Electrical Engineering and Automation (IEA):
Lund University, Sweden; 2011. p. 1–60.
15.Siemens, SIMATIC, Distributed I/O System’ ET 200S, 2005.
p. 1–274.
16.Training document for comprehensive automation solutions. Totally Integrated Automation (TIA), MODULE B3,
Control Engineering with STEP 7; 2008. p. 1–64.
17.Training document for the company-wide automation
solution. Totally Integrated Automation (TIA), MODULE
B2, Analog value processing; 2002 p. 1–14.
18.Chakravarthi MK, Vinay PK, Venkatesan N. Design and
simulation of internal model controller for a real time nonlinear process. Indian Journal of Science and Technology.
2015 Aug; 8(19):1–6.
Indian Journal of Science and Technology
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