International

OPEN ACCESS Journal

Of Modern Engineering Research (IJMER)

DC Motor Modelling and Control using Fuzzy Logic Controller

(FLC)
1
ESMAIL. S. Mohammed, 2SedatNazlibilek,3FATHI SH BENKOURA
1
Lecturer,Mechatronics Engineering,the Higher Institute of Science and Technology, Zawia, Libya
2
Electrical And Electronics Engineering, Baskent University. Ankara, Turkey
3
Lecturer, ElectricalEngineering,the Higher Institute of Science and Technology, Zawia, Libya
Corresponding Author: Esmail. S. Mohammed

ABSTRACT: DC motor position control widely used in industrial applications. This paper was focuses on
the design of a PID controller, fuzzy logic control (FLC) systemis used with PID fuzzy controller and
compared between them for controlling the position of a DC motor. The motor is modelled and simulated.
Moreover the (PID) controller was designed tuned by using a Matlab/Simulink block instead of
conventional tuning methods such as hand-tuning or Ziegler-Nichols method. Then, the fuzzy logic
controller (FLC) was designed and the system responses of (FPID) with different defuzzification methods
were investigated. The signal is angle position (teta) was created by Simulink and applied to the input
control system. FPID controller succeeded to reduce the error between signal input andsignal output is
better than conventional tuning methods.
KEY WARDS: DC motor, position control, hand-tuning method, Fuzzy logic control, FPID controller.

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Date of Submission: 28-01-2019 Date of acceptance: 11-02-2019
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I. INTRODUCTION
Recently, DC motor control to control the motion (speed and position) has become widespread. The
control systems of motors speed and position is very necessary and important because DC motor is widely used
in industrial applications, and many other fields of control systems such as industrial homes place and robotics
where speed and position control of DC motor are required [1-3].Two major problems encountered in DC motor
control are the noise in the system loop and the varying time of the motor parameters under operating
conditions. The PID widespread use of control it is highly desirable to have efficient manual and automatic
methods of tuning the controllers. A good insight into PID tuning is also useful in developing more schemes for
automatic tuning and loop assessment [4]. These methods have successful results but they need more time and
effort to get a good system response. The mathematical model to present of DC motor does not give accurate of
the real system because approximated it to linear system that is main problem [5]. To avoid this problem, fuzzy
logic control (FLC) can be used. The (FLC) does not dependent on the model, also it is insensitive to changing
of parameters [6].The most important advantages of (FLC) is that it can be successfully applied to control
nonlinear systems using an operator experiences or control engineering knowledge without any mathematical
model of the system[7 ]. There are many searches and studies about DC motor fuzzy control system design,
compared PID with FLC for position control and observed that FLC performed better than PID methods and
shown that FLC is less sensitive than PID to load variations [8.9]. In generally some methods realized of DC
motor control by such as adjusting the field resistance, putting a resistor in series with the armature circuit or
adjusting the terminal voltage applied to the armature [10].

1.1 DC motor model

In DC motors armature control the voltage was applied to the field winding (separately excited), the
voltage applied to the armature of the motor is adjusted without changing the voltage applied to the field.
Figure.1 shows a separately excited DC motor equivalent model.

| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 8 | Iss.12 | December 2018 | 33 |

 DC Motor Modelling and Control using Fuzzy Logic Controller (FLC)

Figure 1:Schematic Diagram of DC Motor

The differential equation for the armature circuit is

The equation for torque equilibrium is

By combining the upper equations together:

By taking Laplace transforms to (5) and (6) are:

The armature voltage is

Then the relation between armature voltage and angular speed of the shaft can be presented by transfer function
as

The position can be found as

The transfer function between armature voltage as input and the position of the shaft as output when the motor
without load is:

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 DC Motor Modelling and Control using Fuzzy Logic Controller (FLC)

The DC motor model is built in Simulink/MATLAB as shown in figure.2, the inputs are armature voltage (Va)
and load torque (Tload).The outputs are angular speed in () and position ().

Figure 2. DC motor model in Simulink/MATLAB

The following characteristic of the DC motor was used:.
Performance specifications:
Torque continuous = 0.95 N-m
Peak torque =5.22 N-m
Maximum speed =5000 rpm
Rated power =200 w
Electrical specification
Torque constant = 0.17 N-m/ Amp
Terminal resistance = 1.4 Ohms
BEMF constant = 18.2 V/krpm
Armature inductance = 4.9 m-H
Moment of inertia = 0.00092 kg/m²
Recommended Bus Voltage = 80 VDC
Maximum Terminal voltage =104 VDC

1.2Proportional-integral-derivative (PID) controller

The PID controllers are used mostly in industrial control applications due to their simple structures, completely
control algorithms and low costs. Figure.3 shows the schematic model of a control system with a PID controller.

Figure 3.PID controllers system

Where
KP = proportional gain
KI = integral gain
KD = derivative gain
 DC Motor Modelling and Control using Fuzzy Logic Controller (FLC)

1.3MATLAB/Simulink
The simulation was performed by using Simulink to present the model, the simulation begins with the
motor at the zero degree position. The desired position was 36 degree. There is deferent between input and
output signal without controller as shown in figure 4.

Figure4.Input and output signal without controller

To reduce this deferent, the PID controller was used as shown in Figure.5. By using Hand Tuning, the
parameters was done to reduce the deferent between input signal (position 36 o) and output signal (position). The
values of the parameters which gated the best results as shown in figure.6 are:

Kp = 1.3 Ki = 0.5 Kd =0.5

Figure5.Simulink Block Diagram of DC Motor with PID controller

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 DC Motor Modelling and Control using Fuzzy Logic Controller (FLC)

Figure6.Figure 6. The output response with PID controller

II. Fuzzy logic controller (FLC)

The main components of fuzzy logic controller is fuzzification interface, rule base, inference mechanism, and
defuzzification interface. The fuzzy controller converts a linguistic control strategy into controller strategy, and
fuzzy rules are constructed by expert experience or knowledge database. Firstly, set the error e(t) and the error
variation de(t) of the angular position to be the input variables of the fuzzy logic controller. The control voltage
u(t) is the output variable of the fuzzy logic controller. The linguistic variables are defined as {NB, NS, Z, PS,
PB}, where NB means negative big, NS means negative small, Z means zero, PS means positive small and PB
means positive big.

Figure7. Membership function for (e) normalized input

 DC Motor Modelling and Control using Fuzzy Logic Controller (FLC)

Figure 8. Membership function for (ce) normalized input

Figure 9. Membership function for (u) normalized output

Here max-min type decomposition is used and the final output for system is calculated by using center of area
gravity method.

Where;

j is an index of every membership function of fuzzy set, m is the number of rules and is the inference result.
Fuzzy output u(t) can be calculated by the center of gravity defuzzification as:

III. SIMULINK IMPLEMENTATION

Inputs of FPID are (e) error and (ce) change of error where the output is control. The fuzzy
rules are summarized in Table I. Figures (9-10) shows Fuzzy input variables error (e) and change of
error (ce) and Fuzzy output variable (control) respectively.(a) and (b) consist of seven fuzzy sets
namely NB (negative big), NM (negative medium), NS (negative small), Z (zero), PS (positive small),

| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 8 | Iss.12 | December 2018 | 38 |

 DC Motor Modelling and Control using Fuzzy Logic Controller (FLC)

PM (positive medium) and PB (positive big) . The fuzzy PID control system designed in Simulink as
shown in Figure 11.
Table I. The fuzzy rules are summarized

Figure 9. Fuzzy input variables error (e) and change of error (ce)

Figure 10 . Fuzzy output variable (control)

The figure.11 shows that, the output fuzzy logic controller and output the PID controller. The optimal
performance in tracking the reference input () when fuzzy logic controller FPID was used when compared with
PID controller.

Figure 11. Simulink Block Diagram of DC Motor with fuzzy logic controller
 DC Motor Modelling and Control using Fuzzy Logic Controller (FLC)

Figure 12. Comparing between PID and fuzzy controller

IV. CONCLUSIONS AND RECOMMENDATIONS

The position control of DC motor, PID controlling techniques used Ziegler-Nichols hand tuning and
fuzzy logic controller FPID was presented. The controller shows optimal performance in tracking the reference
input () when fuzzy logic controller FPID was used. Fuzzy Logic Controller provides performance
characteristics and improve the control of DC motor better than conventional tuning method. Hence it is
concluded that the proposed Fuzzy Logic Controller can be used.

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Esmail. S. Mohammed" DC Motor Modelling and Control using Fuzzy Logic

Controller (FLC)" International Journal of Modern Engineering Research (IJMER), vol.
08, no. 12, 2018, pp 33-40

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