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The Proportional Integral Derivative (PID) controller is the most common form
of feedback used in the control systems. It can be used for various Industrial
functions. One of the applications used here is to control the position of the DC
motor. Controlling the position of a DC motor is very significant in that any
small change can lead to instability of the closed loop system. Conventional
PID controllers are used to control the DC motor for various industrial
processes for many years due to their simplicity in operation. PID controllers
require mathematical models to control the plant of different control processing
applications. This paper seeks to focus on controlling the position of DC motors
using PID controller made by PIC microcontroller. Consequently hardware has
been designed and produced which in practice worked well.
PID Controller
PIC Microcontroller
Position Controlling
Control System30 S.Y.Moradi / ZJPAS: 2015, 27(5): 29-36
the system to be controlled, hence
mathematical models of DC motor is derived
(Leonhard and Leonhard 1996, Yeung and
Huang 2003).
1. DC Motor’s Model
DC Motor plays a crucial role in research,
industry and laboratory experiments because of
their low cost. The position of the motor can be
controlled by three methods namely terminal
voltage control method, armature rheostat
control method and flux control method. Here
in this paper terminal voltage control method is
employed. A control system is an
interconnection of components forming a
system configuration that will provide a desired
system response (Rubaai and Kotaru 2000,
Saranya and Pamela 2012).
Figure 1: Components of DC Motor
In armature voltage control scheme for DC
motors, voltage applied to armature is varied
without varying the voltage applied to the field
(Inaba, Shima et al. 1978, Xue, Zhao et al.
2006). Equivalent model of DC motor is shown
in following figure (Figure 2).
Figure 2: DC Motor Model
Where (Xue, Zhao et al. 2006, Rao, Obulesu
etal. 2010, Zouari, Saad et al. 2012)
= Armature Voltage (V)
= Armature Resistance (Ω)
= Armature Inductance (H)
= Armature Current (A)
= Back EMF (V)
= Angular Speed (rad/s)
= Motor Torque (Nm)
θ = Angular Position of Rotor Shaft (rad)
= Inertia of Rotor (Kg-m2)
= Viscous Friction Coefficient (Nms/rad)
= Torque Constant (N-m/A)
= Back EMF Constant (V/rad)
We combine the upper equations together:
Laplace transforms and then the transfer
function between shaft position and armature
voltage at no-load is:
Equation 7: DC Motor Transfer Function
2. PID controllers
A proportional–integral–derivative controller
(PID controller) is a generic control loop
feedback mechanism (controller) widely used
in industrial control systems. A PID controller31 S.Y.Moradi / ZJPAS: 2015, 27(5): 29-36
calculates an "error" value as the difference
between a measured process variable and a
desired set point. The controller attempts to
minimize the error by adjusting the process
control inputs (Kaya 2004, Xue, Zhao et al.
2006). A PID controller consists of a
proportional element, an Integral element and a
derivative element, all three connected in
parallel. All of them take the error as input. Kp,
Ki, Kd are the gains of P, I and D elements
respectively. In Figure 1 a schematic of a
system with a PID controller is shown (Xue,
Zhao et al. 2006, Rao, Obulesu et al. 2010).
The PID controller compares the measured
process value with a reference set point value.
The difference or error, “e”, is then processed
to calculate a new input process. This input
will try to adjust the measured process value
back to the desired set point. The alternative to
a closed loop control scheme such as the PID
controller is an open loop controller. Open loop
control (no feedback) is in many cases not
satisfactory, and is often impossible due to the
system properties. By adding feedback from
the system output, performance can be
improved (Saranya and Pamela 2012).
Figure 3: Closed Loop System with PID controller
Unlike a simple proportional control algorithm,
the PID controller is able to manipulate the
input processes based on the history and the
rate of change of the signal. This gives a more
accurate and stable control method (Leonhard
2001, Xue, Zhao et al. 2006, Petráš 2009). The
basic idea is that the controller reads the system
state by a sensor. Then it subtracts the
measurement from a desired reference to
generate the error value. The error will be
managed in three ways, to: 1. Handle the
present, through the proportional term, 2.
Recover from the past, using the integral term,
3. Anticipate the future, through the derivative
term. In this paper we will use a basic open
loop system and a closed loop system with PID
4. Conclusion
The objective of this experiment is to design a
closed-loop control system that regulates the
position of the DC motor. The mathematical
model of a DC motor is reviewed and its
physical parameters are identified. Once the
model is verified, it is used to design a (PID)
proportional-integral-derivative controller.
In this paper PID controller is designed for
position control of DC motor. According to our
analysis it is clear that PID is a simple
controller based on the mathematical model of
the system. It successfully overcomes the
drawback of proportional-derivative (PD)
controller of steady-state error as steady-state
error is zero in PID controllers. However while
reducing steady-state error to zero, an
overshoot is observed. Overshoot can be
reduced by increasing the derivative gain but
the rise time also increases as a consequence.
Hence, there is a compromise between
overshoot and the speed of response i.e. rise
time which means we have to sacrifice one for
improving another. Overall, PID controller
gives best speed response of all the linear
controllers of its class.
Typical applications of this hardware:
 Compressors and fans in refrigerators
 Fans in cooker hoods
 Drums and pumps in washing machines
 Robotic Arms
 Surgery Robots
 Fan controller
 Medical Application
8. Rao, A. P. C., Y. Obulesu and C. S. Babu (2010).
"Robust Internal Model Control Strategy based PID
Controller for BLDCM." International Journal of
Engineering Science and Technology 2(11): 6801-6811.
9. Rubaai, A. and R. Kotaru (2000). "Online identification
and control of a DC motor using learning adaptation of
neural networks." Industry Applications, IEEE
Transactions on 36(3): 935-942.
10. Saranya, M. and D. Pamela (2012). "A real time IMC
tuned PID controller for DC motor." International
Journal of Recent Technology and Engineering 1(1): 65-
11. Xue, D., C. Zhao and Y. Chen (2006). Fractional order
PID control of a DC-motor with elastic shaft: a case
study. Proceedings of American control conference.
12. Yeung, K. and J. Huang (2003). "Development of a
remote-access laboratory: a dc motor control
experiment." Computers in Industry 52(3): 305-311.
13. Zouari, F., K. B. Saad and M. Benrejeb (2012).
"Adaptive Internal Model Control of a DC Motor Drive
System Using Dynamic Neural Network." Journal of
Software Engineering and Applications 5(03): 168.

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