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Engineering, Technology & Applied Science Research Vol. 11, No. 4, 2021, 7399-7404 7399 
 

www.etasr.com Mugheri & Keerio: An Optimal Fuzzy Logic-based PI Controller for the Speed Control of an Induction … 

 

An Optimal Fuzzy Logic-based PI Controller for the 

Speed Control of an Induction Motor using the V/F 

Method 
  

Noor Hussain Mugheri 

Department of Electrical Engineering 
Quaid-e-Awam University of Engineering Science and 

Technology 

Nawabshah, Sindh, Pakistan 
noorhussain@quest.edu.pk 

Muhammad Usman Keerio 

Department of Electrical Engineering 
Quaid-e-Awam University of Engineering Science and 

Technology 

Nawabshah, Sindh, Pakistan 
usmankeerio@quest.edu.pk

 

Abstract-The Induction Motor (IM) is popular because of its low 

price, higher efficiency, and low maintenance cost. A comparative 

analysis of IM speed controllers using Voltage/Frequency (V/F) 
control or Scalar Control (SC) is presented in this paper. SC is 

commonly used due to its ease of implementation, simplicity, and 

low cost. To decrease the difficulty and cost of hardware 

implementation, this paper proposes an optimal Fuzzy 

Proportional Integral (Fuzzy-PI) controller. Firstly, the speed of 

IM using the V/F control technique is discussed. Then, speed 

control of IM using a conventional PI controller is performed. 
Finally, a simplified-rules Fuzzy-PI controller is developed in 

MATLAB/SIMULINK and its performance is compared with 

that of open-loop SC and the traditional PI controller. The 

performance of the simplified-rules Fuzzy-PI controller is 

superior to that of an open-loop constant V/F control and a 
conventional PI controller. 

Keywords-induction motor; constant V/F control; PI controller; 

optimal fuzzy PI controller 

I. INTRODUCTION  

Its low cost, simple structure, reliability, and good 
robustness have made the Induction Motor (IM) a most 
attractive choice [1, 2] for industry applications. To change the 
speed of IMs, frequency and voltage can be varied. In 
Voltage/Frequency (V/F) control, only their magnitudes are 
controlled. V/F control is easy to implement, it requires a small 
number of components, and can be employed in several 
applications such as variable speed pumps, fans, blowers, etc. 
[3]. Normally, a Proportional Integral (PI) controller is 
employed for IM speed control for most applications [4, 5]. 
But, the conventional PI controller has some disadvantages: Its 
accuracy depends on the mathematical model, the system 
suffers fromnon-linearity, and it is very sensitive to parameter 
and temperature variations and to load disturbances [6, 7]. 

A Fuzzy Logic-based Controller (FLC) can overcome these 
disadvantages [8-10]. The FLC does not need the model of the 
plant, can handle non-linearity, it is less sensitive to load 
disturbances, it produces human logic linguistic rules, and is 
robust [11-13]. Authors in [14] suggested a 3-phase IM speed 

control technique using constant V/F control. A simplified 
fuzzy rule-based FLC can be easily implemented in hardware 
and performs better in comparison with the standard 25-rule 
FLC [15]. D. Asija proposed a standard 25-rule Fuzzy-PI 
controller for IM speed control using SC and concluded that the 
Fuzzy-PI controller outperforms the traditional PI controller 
[16]. B. N. Kar and K. B. Mohanty presented a standard 49-
rule FLC for IM speed control by Indirect Vector Control 
(IVC) and concluded that compared with the conventional PI 
controller, the proposed FLC performs better with regard to 
change in load, settling time, and overshoot [17]. Authors in 
[18] proposed a standard 49-rule FLC based IM speed control 
by IVC and concluded that FLC performs better than the 
traditional PI controller with regard to load disturbances and 
changing reference speed. Authors in [19] proposed and 
implemented a 3-phase IM using V/F control.  

II. PROPOSED FRAMEWORK 

A simplified-rule Fuzzy-PI controller using the V/F control 
technique is presented in this paper. Various researchers 
proposed FLC-based IM speed control using standard 49 fuzzy 
rules, standard 25 rules, standard 9 rules, and simplified fuzzy 
rules [20-25]. In this paper, the standard 9 rules are simplified 
into 5 rules for the first time and the simplified-rule Fuzzy-PI 
controller is developed and implemented for the speed control 
of a 3-phase IM using constant V/F control. The rule base is 
selected using the trial and error technique. The proposed 
simplified Fuzzy-PI controller reduces complexity and 
computational load, it is easily implemented, it is simple, and 
has better performance. Figure 1 exhibits the diagram of the 
proposed framework. The DC supply voltage is converted into 
variable voltage and variable frequency by a Metal Oxide 
Semiconductor Field Effect Transistor (MOSFET) inverter. 
The optimal Fuzzy-PI controller gets feedback signals from an 
IM. The method of speed control used here is the popular 
constant V/F control. The SVPWM generator produces firing 
pulses to the inverter. The MOSFET converts the DC supply 
voltage into variable AC by the SVPWM method.  

 

Corresponding author: Noor Hussain Mugheri 



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Fig. 1.  The proposed framework. 

III. CONSTANT V/F CONTROL 

With the recent developments in power electronics, variable 
voltage variable frequency IMs are increasingly employed in a 
variety of industrial applications. The circuit diagram of V/F 
control is shown in Figure 2. Speed control of IM is achieved 
using a 3-phase inverter. The frequency and applied voltage 
must be varied to maintain constant air-gap flux and to evade 
saturation of the IM. The stator voltage and frequency are 
changed at the same time to keep V/F ratio constant.  

 

 
Fig. 2.  Constant V/F control. 

IV. THE FUZZY LOGIC CONTROLLER 

The FLC is an intelligent controller that is very similar to 
human reasoning. It accepts crisp input, performs calculations, 
and then gives an output value [26]. The structural diagram of 
the FLC is shown in Figure 3. There are four steps for the 
implementation of an FLC: Fuzzification, inference engine, 
rule base, and de-fuzzification. The FLC inputs are 
characterized by triangular Membership Functions (MFs) and 3 
triangular MFs are used for output control. There are 9 rules 
out of which 5 are executed by the Fuzzy Inference System 
(FIS). The triangular MFs for the FLC system are shown in a 
Figure 4. The MFs for two inputs and a single output are: 
Positive (P), Zero (Z), and Negative (N). 

 

 
Fig. 3.  The FLC structural block diagram. 

(a) 

 

(b) 

 

(c) 

 

Fig. 4.  The triangular membership functions: (a) error (e), (b) change of 
error (∆e), (c) Output control (∆u). 

Table I shows the simplified rule base for the FLC. For this 
application the Mamdani type FIS is chosen. The fuzzy logic 
process which is based on if-then rules is represented as 
follows: If N and P denote the error and change of error 
respectively then the output control will be denoted by Z. 

TABLE I.  THE SIMPLIFIED FUZZY RULES FOR THE FLC 

e ∆e N Z P Simplified Rules 

1. If e is N and ∆e is Z then ∆u is N 
2. If e is Z and ∆e is P then ∆u is P 
3. If e is Z and ∆e is Z then ∆u is Z 

4. If e is Z and ∆e is N then ∆u is N 
5. If e is P and ∆e is N then ∆u is Z 

P Z P P 

Z N Z P 

N N N Z 

 

 

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

error

D
e
g
re
e
 o
f 
m
e
m
b
e
rs
h
ip

N Z P

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

ChangeOfError

D
e
g
re
e
 o
f 
m
e
m
b
e
rs
h
ip

N Z P

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Outputcontrol

D
e
g
re
e
 o
f 
m
e
m
b
e
rs
h
ip

N Z P



Engineering, Technology & Applied Science Research Vol. 11, No. 4, 2021, 7399-7404 7401 
 

www.etasr.com Mugheri & Keerio: An Optimal Fuzzy Logic-based PI Controller for the Speed Control of an Induction … 

 

 
Fig. 5.  The SIMULINK diagram of the open loop V/F control-based IM speed control. 

(a) 

 

(b) 

 

Fig. 6.  The open loop IM speed control: (a) rotor speed, (b) 
electromagnetic torque. 

V. RESULTS AND DISCUSSION 

Figure 5 depicts the SIMULINK diagram of the open loop 
IM speed control using the SVPWM technique. A 3-phase IM 
is fed from an inverter and is connected with a DC voltage 
source. A MOSFET inverter is modeled by a universal block 
and the IM by an asynchronous machine block. A constant 
value of 11.9Nm load torque is applied to the IM shaft. A speed 
set point in rpm is applied to the V/Hz block. The initially 
reference speed is 1725rpm and the final speed value is 
1300rpm. The induction motor reaches a speed of 1275rpm. In 
this application, IM does not reach the final speed of 1300rpm 
which is the required final value. The simulation results for 
open loop scalar control are shown in a Figure 6. 

The SIMULINK diagram of the traditional PI controller 
based IM closed loop speed control using SVPWM technique 
is shown in Figure 7. The Zeigler-Nichols method is used in 
this paper for tuning the PI controller. To study the 
performance of the 3-phase IM, two performance indices, i.e. 
rotor speed and electromagnetic torque are used. In order to 
achieve the actual IM speed, feedback is used and it is 
compared to the IM reference speed. The error is produced by 
the obtained difference of the two and the error is processed in 
a conventional PI controller which reduces it. The same 
reference step speed and load torque is used for the traditional 
PI controller. The SIMULINK diagram of the simplified 
Fuzzy-PI controller using SC is given in Figure 9. The error 
signal and the change of error are the input of an intelligent 
controller. The Fuzzy-PI controller produces the controlled 
output signal and provides it to the V/F control. 

0 0.5 1 1.5
1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

1750

Time (sec)

S
p
e
e
d
 (
re
v
/m

in
)

0 0.5 1 1.5
5

6

7

8

9

10

11

12

13

14

Time (sec)

T
o
rq
u
e
 (
N
m
)



Engineering, Technology & Applied Science Research Vol. 11, No. 4, 2021, 7399-7404 7402 
 

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Fig. 7.  The SIMULINK model of the conventional PI controller based IM speed control. 

(a) 

 

(b) 

 

Fig. 8.  The simulation results for conventional PI controller based IM 
speed control: (a) rotor speed, (b) electromagnetic torque. 

Here, the frequency varying device is the SVPWM 
generator which uses the SVPWM technique to produce firing 
pulses to the inverter and hence the frequency of the supply is 
changed w.r.t the voltage of the IM. Using the SVPWM 
method, the MOSFET inverter converts the DC supply voltage 
into variable AC. From Figure 8, it is clear that the 
conventional PI controller reaches the final speed of 1300rpm. 
From Figure 10, it is clear that the optimal Fuzzy-PI controller 
also achieves the final speed of 1300rpm but with less settling 
time and without overshoot compared with a conventional PI 
controller and also better electromagnetic torque response is 
obtained. When compared with the existing literature, the 
proposed simplified Fuzzy-PI controller uses the least number 
of fuzzy rules. The proposed intelligent controller employs a 
trial and error approach to minimize the fuzzy rules while 
achieving better IM speed control performance. Figure 11 
depicts the speed response of the IM with step speed reference 
with both controllers and constant V/F control. The 
performance analysis of both controllers and without any 
controller at a constant reference speed is given in Table II. 

TABLE II.  PERFORMANCE ANALYSIS 

 
Settling 

time (sec) 

Overshoot 

(%) 

Load 

(Nm) 

Conventional PI 

controller 
1.440 0.307 

11.9 
Optimal Fuzzy-PI 

controller 
0.980 0 

Without controller 
Does not 

settle 
---- 

0 0.5 1 1.5
1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

1750

Time (sec)

S
p
e
e
d
 (
re
v
/m

in
)

0 0.5 1 1.5
5

6

7

8

9

10

11

12

13

14

Time (sec)

T
o
rq
u
e
 (
N
m
)



Engineering, Technology & Applied Science Research Vol. 11, No. 4, 2021, 7399-7404 7403 
 

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Fig. 9.  The SIMULINK diagram of the optimal Fuzzy-PI controller based IM speed control. 

(a) 

 

(b) 

 

Fig. 10.  The simulation results for the optimal Fuzzy-PI controller based 
IM speed control: (a) rotor speed, (b) electromagnetic torque. 

VI. CONCLUSION 

To reduce the computation load, the need of memory space, 
and to enable more easy hardware implementation an optimal 
Fuzzy-PI controller using SC is successfully developed and 
simulated in this paper. The proposed optimal Fuzzy-PI 
controller uses a minimum number of fuzzy rules. The 
simulation results show that the performance and speed 

response of the simplified-rule set Fuzzy-PI controller surpass 
the ones of the open loop constant V/F control and the 
traditional PI controller. The optimal Fuzzy-PI controller fully 
eradicates the overshoot of the conventional PI controller and 
the settling time is much smaller than that of the traditional PI 
controller.  

 

 
Fig. 11.  Speed response of the 3-phase IM with step speed reference with 

an initial value of 1725rpm and final speed reference of 1300rpm at 0.1s and 

11.9Nm load with optimal Fuzzy-PI controller, conventional PI controller, and 
without controller. 

ACKNOWLEDGEMENT 

This research was funded by the Quaid-e-Awam University 
of Engineering, Science and Technology, Nawabshah, Sindh, 
Pakistan. 

0 0.5 1 1.5
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Time (sec)

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in
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0 0.5 1 1.5
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Time (sec)

T
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APPENDIX 

IM ELECTRICAL PARAMETERS 

Power = 3hp , Voltage = 220V, Poles = 4, Frequency = 60Hz 

RS = 0.435Ω, Rr = 0.815Ω 

LS = 2*2.0mH, Lr = 2.0mH 

Lm = 69.31mH 

J = 0.089 Kg.m
2 

 

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