Improvements the direct torque control performance for an induction machine using fuzzy logic controller

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 1~11
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp1-11  1
Journal homepage: http://ijece.iaescore.com
Improvements the direct torque control performance for an
induction machine using fuzzy logic controller
Radouan Gouaamar1
, Seddik Bri1
, Zineb Mekrini2
1
Materials and Instrumentation, Electrical Engineering, High School of Technology, Moulay Ismail University, Meknes, Morocco
2
Industrial Systems Engineering and Energy Conversion Team, Faculty of Sciences and Technology of Tangier,
Abdelmalek Essaadi University, Tetouan, Morocco
Article Info ABSTRACT
Article history:
Received May 21, 2023
Revised Jul 16, 2023
Accepted Jul 17, 2023
This article examines a solution to the major problems of induction machine
control in order to achieve superior dynamic performance. Conventional
direct torque control and indirect control with flux orientation have some
drawbacks, such as current harmonics, torque ripples, flux ripples, and rise
time. In this article, we propose a comparative analysis between previous
approaches and the one using fuzzy logic. Results from the simulation show
that the direct torque control method using fuzzy logic is more effective in
providing a precise and fast response without overshooting, and it eliminates
torque and flux fluctuations at low switching frequencies. The demonstrated
improvements in dynamic performance contribute to increased operational
efficiency and reliability in industrial applications.
Keywords:
Direct torque control
Flux ripple
Fuzzy logic control
Indirect field-oriented control
Induction machines
Torque ripple
This is an open access article under the CC BY-SA license.
Corresponding Author:
Radouan Gouaamar
Materials and Instrumentation, Electrical Engineering, High School of Technology, Moulay Ismail University
Meknes, Morocco
Email: radouan.gouaamar@gmail.com
1. INTRODUCTION
Electric motors are essential components in the industrial sector, with three-phase asynchronous
motors being particularly significant. They account for approximately 80% of industrial control systems [1],
[2]. These motors are highly valued for their reliability, straightforward design, cost-effectiveness, and
minimal maintenance requirements. However, their modeling presents challenges due to their dynamic and
nonlinear nature, complex equations, and difficult-to-measure state variables [3], [4]. Consequently,
advanced control algorithms are necessary to ensure effective real-time control of torque and flux [5], [6].
One prominent control technique developed in the 1970s is field-oriented control (FOC), which offers the
advantage of decoupling electromagnetic torque and flux. This enables a prompt torque response, a broad
range of speed regulation, and high efficiency across a variety of loads [7], [8]. Nevertheless, field-oriented
control (FOC) can be intricate and susceptible to motor parameter variations [9]. To address these challenges,
a novel approach known as direct torque control (DTC) was first introduced by Takahashi and Noguchi in
the early 1980s [10]. DTC has attracted considerable attention due to its advantageous features, including a
straightforward structure, dynamic response, and reduced reliance on machine parameters. Importantly, DTC
eliminates the necessity for current regulation and coordinate transformations [11], [12].
However, there are two significant drawbacks to this approach: uncontrolled flux and torque ripples,
as well as variable switching frequency [13], [14]. These ripples create additional noise and vibrations, which
can result in fatigue and wear of the machine shaft. In order to mitigate these effects, the integration of
intelligent techniques is seen as advantageous [15], [16].

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 1-11
2
This article focuses on investigating the potential of utilizing fuzzy logic-based DTC to enhance the
performance of current control methods. Our research suggests using a fuzzy logic controller in place of the
switching selection block and the two hysteresis controllers. By implementing this fuzzy logic controller, the
output parameters of the asynchronous machine can be effectively regulated to their desired reference values
within a predetermined timeframe. The primary objectives of our study revolve around achieving the
following goals: minimizing response time, reducing flux and torque oscillations, and reducing the stator
current's overall harmonic distortion (THD).
This article is organized manner: section 2 provides a detailed introduction to the modeling of the
asynchronous motor, offering a comprehensive overview of its characteristics. In section 3, the control
method is extensively discussed, emphasizing its fundamental principles and key features. To verify and
assess the performance of the suggested strategy, section 4 presents the simulation results obtained using the
MATLAB/Simulink environment. This section allows for a meticulous examination and comparison of the
outcomes. The article concludes in section 5 with a summary of the key conclusions drawn from the research
and helpful suggestions for future research directions.
2. INDUCTION MOTOR MODEL
It is generally acknowledged that the (α, β) reference frame, which symbolizes a two-phase model,
is the best option for analyzing the dynamic behavior and developing control schemes for three-phase
induction devices. This strategy makes the Triple-phase representation of the machine less complicated. The
following are possible expressions for the electromagnetic equations that control induction motors [17]:
𝑑
𝑑𝑡
[
𝑖𝑠𝛼
𝑖𝑠𝛽
𝜓𝑠𝛼
𝜓𝑠𝛽]
=
[
−
1
𝛼
(
1
𝜏𝑠
+
1
𝜏𝑟
)
𝑤𝑟
−𝑅𝑠
0
−𝑤𝑟
−
1
𝛼
(
1
𝜏𝑠
+
1
𝜏𝑟
)
0
−𝑅𝑠
1
𝛼𝐿𝑠𝜏𝑟
−
𝑤𝑟
𝛼𝐿𝑠
0
0
𝑤𝑟
𝛼𝐿𝑠
1
𝛼𝐿𝑠𝜏𝑟
0
0 ]
.
[
𝑖𝑠𝛼
𝑖𝑠𝛽
𝜓𝑠𝛼
𝜓𝑠𝛽]
+
[
1
𝛼𝐿𝑠
0
0
1
𝛼𝐿𝑠
1
0
0
1 ]
. [
𝑣𝑠𝛼
𝑣𝑠𝛽
] (1)
In the equations provided, the following variables and parameters are defined: M is mutual inductance,
𝑤𝑟 is rotor mechanical angular velocity, 𝐿𝑠 is stator inductance, 𝐿𝑟 is rotor inductance, 𝑅𝑠 is stator resistance, Ω
is mechanical speed, 𝑖𝑠𝛼,𝛽 is currents of the stator, 𝑣𝑠𝛼,𝛽 is stator voltage, 𝜓𝑠𝛼,𝛽 is stator flux, J is inertia
moment, and P is number of pole pairs. The parameters α, τr, and τs are defined as positive constants:
𝜏𝑟 =
𝐿𝑟
𝑅𝑟
; 𝛼 = 1 −
𝑀2
𝐿𝑠𝐿𝑟
; 𝜏𝑠 =
𝐿𝑠
𝑅𝑠
(2)
The expressions for the electromagnetic torque and motion equations are as in (3), (4).
𝑇𝑒𝑚 =
3
2
𝑝(𝜓𝑠𝛼𝑖𝑠𝛽 − 𝜓𝑠𝛽𝑖𝑠𝛼) (3)
𝐽.
𝑑𝛺
𝑑𝑡
+ 𝑓. 𝛺 = 𝑇𝑒𝑚 − 𝑇𝑟 (4)
3. CONTROL METHOD
3.1. Direct torque control
Middle of the 1980s, Takahashi and Depenbrock first suggested the idea of direct torque control
(DTC) as a control strategy for induction machines (IM). DTC offers several advantages over vector control,
including reduced sensitivity to changes in machine parameters. It also features a simpler control algorithm
that does away with the requirement for pulse width modulation (PWM), current controllers, or Park
transforms. Moreover, DTC eliminates proportional-integral (PI) control loops, thereby enhancing dynamic
performance and mitigating issues caused by proportional-integral regulator saturation. This enables DTC to
achieve fast and accurate torque response, facilitating high-efficiency operation [18], [19].
The electromagnetic torque (𝑇𝑒𝑚) and flux (𝜓𝑠) of a machine can be controlled using the DTC method.
The voltage inverter switches connected to the machine are directly controlled through a control sequence. To
enable decoupled control and regulation of the machine's torque and flux, DTC makes use of two hysteresis
regulators, a switching table, and other parts. Figure 1 illustrates the structural layout of the DTC control
system. A three-level hysteresis controls the magnetic torque, while a two-level hysteresis regulates the flux. To
create the best switching table, these comparators' outputs are combined with knowledge of the flux vector.

Int J Elec & Comp Eng ISSN: 2088-8708 
Improvements the direct torque control performance for an induction … (Radouan Gouaamar)
3
Figure 1. Diagram illustrating DTC for an induction motor
The estimations of 𝑇𝑒𝑚 and the 𝜓𝑠 are computed using (5) and (6).
𝜓𝑠 = √𝜓𝑠𝛼
2
+ 𝜓𝑠𝛽
2
(5)
𝑇𝑒𝑚 = 𝑝(𝜓𝑠𝛼𝑖𝑠𝛽 − 𝜓𝑠𝛽𝑖𝑠𝛼) (6)
The angle 𝜃𝑠 is derived through calculation from (7).
𝜃𝑠 = 𝑎𝑟𝑐𝑡𝑔(
𝜓𝑠𝛼
𝜓𝑠𝛽
) (7)
The reference (α, β) provides the stator flux components as in (8) and (9).
𝜓𝑠𝛼 = ∫ (𝑉
𝑠𝛼 − 𝑅𝑠𝐼𝑠𝛼)𝑑𝑡
𝑡
0
(8)
𝜓𝑠𝛽 = ∫ (𝑉𝑠𝛽 − 𝑅𝑠𝐼𝑠𝛽)𝑑𝑡
𝑡
0
(9)
The creation of inputs for hysteresis comparators and their related reference values, 𝜓𝑠,𝑟𝑒𝑓, is made
possible by the evaluation of the predicted stator 𝜓𝑠,𝑒𝑠𝑡 and projected electromagnetic torque 𝑇𝑒𝑚. The inputs
from these comparators, which include things like the flux sector number of the hysteresis comparator and its
outcomes, are vital for the control table as shown in Table 1. Utilizing this information, the control table
selects the appropriate voltage vector (𝜇0 to 𝜇7) for subsequent processing, ensuring effective control and
regulation of the system.
Table 1. Switching table
eψ eTe S(1) S(2) S (3) S(4) S(5) S(6)
1 1 μ2 [110] μ3 [010] μ4 [011] μ5 [001] μ6 [101] μ1[100]
1 0 μ7 [111] μ0 [000] μ7 [111] μ0 [000] μ7 [111] μ0 [000]
1 -1 μ6 [101] μ1 [100] μ2 [110] μ3 [010] μ4 [011] μ5 [001]
-1 1 μ3 [010] μ4 [011] μ5 [001] μ6 [101] μ1 [100] μ2 [110]
-1 0 μ0 [000] μ7 [111] μ0 [000] μ7 [111] μ0 [000] μ7 [111]
-1 -1 μ5 [001] μ6 [101] μ1 [100] μ2 [110] μ3 [010] μ4 [011]
3.2. Indirect flux-oriented control
Field-oriented control is a widely adopted approach for variable speed drives of asynchronous
machines, known for its ability to provide precise speed and torque control, along with high static and
dynamic performance. Similar to a regular motor, it provides a great transient response and permits

 ISSN: 2088-8708
4
independent control of the electromagnetic flux and torque. A coordinate system (d-q) that is aligned with the
rotating 𝜓𝑑𝑟 is used to implement the control in FOC. The quadrature component 𝜓𝑞𝑟 is deleted while the
flux's direct component is preserved since the d-axis corresponds with the rotor 𝜓𝑟. This decoupling between
the excitation current controlling the armature current linked to the torque and the flux allows for effective
control of both quantities, resembling the characteristics observed in DC motors [20], [21]. The following
orientation condition expresses this:
𝜓𝑞𝑟 = 0 ; 𝜓𝑑𝑟 = 𝜓𝑟 = 𝑀. 𝐼𝑠𝑑 (10)
An expression for electromagnetic torque is:
𝑇𝑒𝑚 =
3
2
𝑀
𝐿𝑟
𝑃𝜓𝑟𝐼𝑠𝑞 (11)
Analyzing the equations in the following system will yield the control formulas as in (12) and (13).
𝐼𝑠𝑑_𝑟𝑒𝑓 =
1
𝑀
𝜓𝑟_𝑟𝑒𝑓 (12)
𝐼𝑠𝑞_𝑟𝑒𝑓 =
2
3
𝐿𝑟
𝑃𝑀
𝑇𝑒𝑚
𝜓𝑟_𝑟𝑒𝑓
(13)
The proposed indirect flux-oriented control (IFOC) technique employed in this study eliminates the
need for rotor flux magnitude estimation. Instead, a flux-weakening block is utilized to control the flux in an
open loop fashion, where a reference value is set. The position of the stator frequency 𝑤𝑠 is determined by
integrating the calculated speed and slip frequency. This approach simplifies the control strategy by
removing the requirement for direct measurement or estimation of the rotor flux magnitude, enhancing the
overall efficiency and robustness of the control system.
θs = ∫ ws. dt = ∫ (wr +
M
Tr
Isq_ref
ψr_ref
) dt (14)
In order to achieve speed regulation, a PI-type controller is implemented to minimize the deviation
between the desired speed and the estimated speed. Furthermore, to control the direct and quadrature stator
currents, two PI controllers are used. To address the interactions between the two orthogonal axes, a
decoupling block is introduced, ensuring proper decoupling and control of the d and q axes. This approach
allows for precise control of the stator currents, leading to improved performance and efficiency of the
system. The current controllers extract the voltage references necessary to achieve the desired flux and
electromagnetic torque through an inverse park transformation. Subsequently, the voltage waveforms
required are generated, and the sine-triangle PWM technique is employed to drive the voltage inverter.
Figure 2 illustrates a diagram of the comprehensive system model, incorporating the developed controller for
the induction motor controlled by IFOC.
Figure 2. The vector control diagram for an induction motor

5
3.3. Fuzzy logic-based torque control
In the context of controlling an asynchronous motor driven by a two-level voltage inverter, Figure 3
shows a DTC fuzzy logic control approach. To determine electromagnetic torque and the stator flux at each
sampling interval, the measured stator current and applied voltage vectors (15) to (17) are utilized. However,
the accuracy of the response is not always guaranteed, especially when dealing with minor faults and when
the same switch state used for normal operation is applied to handle severe issues. To enhance system
performance, the voltage vector can be calculated based on the torque mistake and flux error data. A fuzzy
logic controller, as opposed to a hysteresis comparator and a typical switching table, is used to accomplish
this. The three inputs for the proposed fuzzy logic controller (FLC) are the stator flux mistake, the angle
indicating the location of the flux, and the torque error. Sa, Sb, and Sc are three other outputs that it has in
order to boost system performance. Figure 4 depicts a block diagram of how fuzzy logic works. It is
composed of three major components: fuzzification, inference, and defuzzification [22].
𝛥𝑇𝑒 = 𝑇𝑒_𝑟𝑒𝑓 − 𝑇𝑒 (15)
𝛥𝜓𝑠 = 𝜓𝑠_𝑟𝑒 − 𝜓𝑠 (16)
𝜃𝑠 = 𝑎𝑟𝑐𝑡𝑎𝑛 (
𝜓𝑠𝛽
𝜓𝑠𝛼
) (17)
Figure 3. Diagram of DTC-fuzzy control for induction machine: a synoptic overview
Figure 4. Schematic of a fuzzy controller

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3.3.1. Fuzzification
The FLC takes physical input variables and fuzzifies them by converting them into linguistic
variables, which involves specifying membership functions for each input variable as depicted in Figure 5.
For the first input variable, flux error, N and P, two linguistic variables are assigned to represent positive and
negative flux errors. Figure 5(a) displays the trapezoidal membership functions selected for the two fuzzy
sets (P and N). The electromagnetic torque error is the next input parameter, N stands for null torque error, P
for positive torque mistake, and Z for positive torque error as shown in Figure 5(b). The membership
functions for the sets are represented using trapezoidal and triangular functions in Figure 4. The third input
variable is the location of the flux in the stator vector, and it is separated into six fuzzy sets (𝜃1 to 𝜃6), each
with a triangular membership function is shown in Figure 5(c). the output variable representing inverter
switching state is divided into three output groups (Sa, Sb, and Sc), with each group's discourse universe being
divided into two fuzzy sets (0 and 1) using a singleton form membership function [23], [24].
Figure 5. Membership function inputs:(a) Stator flux error, (b) Torque error, and (c) Flux position
3.3.2. Fuzzification block
In the suggested control scheme for induction machine systems, the FLC block is a key component.
This block is responsible for handling input variables, converting them into appropriate linguistic values, and
providing output variables that represent the inverter switching states. To make sure the control procedure is
accurate, the FLC block first establishes the value ranges for the input variables' membership functions.
Triangular and trapezoidal sets are used to represent membership functions, and fuzzy sets Z, N, and P are
employed to explain 𝑇𝑒𝑚 error and the 𝜓𝑠 fault. Moreover, to succeed precise control over the stator flux
angle sector, the FLC block divides it into six sets of fuzzy values denoted as 𝜃𝟏 to 𝜃𝟐. Moreover, to achieve
precise control over the stator flux angle sector, the FLC block divides it into six sets of fuzzy values denoted
as 𝜃1 to 𝜃2. These fuzzy sets reflect the stator flux distribution over six sectors in the (α, β) standard frame,
enabling precise flux control. Finally, the output variable, which represents the inverter switching states, is
divided into three output singletons (S1, S2, and S3) based on two fuzzy sets, zero and one. This categorization
ensures effective control over the inverter switching operations, resulting in superior dynamic performance
and reduced torque and flux ripples. In Table 2, we group together 36 fuzzy rules that are determined by
using membership functions of input variables to select the appropriate switching state [25], [26].
Table 2. Switching fuzzy rules
𝑒𝜓 𝑒𝑇𝑒 𝜃1 𝜃2 𝜃3 𝜃4 𝜃5 𝜃6
P P 110 010 011 001 101 100
P Z 111 000 111 000 111 000
P N 101 100 110 010 011 001
N P 010 011 001 101 100 110
N Z 000 111 000 111 000 111
N N 001 101 100 110 010 011

7
3.3.3. Foundations of control rules and inference mechanisms
The fuzzy controller employs linguistic variables to draw inferences based on a set of rules. The rule
base encapsulates the operator's knowledge in controlling the process. By utilizing linguistic rules, a fuzzy
rule system enables the description of a transfer function between input and output variables. In this
particular case, the fuzzy control system comprises 36 rules, outlined in Table 1. The inference method
employed is the Mamdani method, which utilizes the Max-Min approach for decision-making. The system’s
most important block also specifies the value ranges for the functions that determine the membership of the
output variables. After that, this block fuzzifies the inferred fuzzy signal in order to provide a non-fuzzy
control signal. The most well-known and often employed techniques for this procedure entail figuring out the
center of gravity and maximum values. The latter technique was used in this investigation. Figure 6 displays
membership formulas for voltage vector membership functions in the output space.
Figure 6. Output membership functions
4. RESULTS AND DISCUSSION
Figures 7 to 18 illustrate the simulation outcomes for the suggested control techniques, which
include indirect field-oriented control, DTC utilizing hysteresis controllers, and fuzzy logic-based DTC of an
IM powered by inverter. The speed is fixed at 156 rad/s, with a load torque of 0 Nm from 0 to 0.2 seconds,
10 Nm from 0.2 to 0.7 seconds, and 5 Nm from 0.7 to 1 second. This study compares the effectiveness of
these control methods for an induction machine, considering stator flux, stator current, speed, response time,
and electromagnetic torque as the evaluation criteria. With regards to speed, indirect field-oriented control, as
shown in Figure 7, generally exhibits longer response times due to its reliance on flux and torque regulators.
Traditional direct control, as shown in Figure 8, demonstrates shorter response times as it allows for more
direct control of motor speed.
Figure 7. Rotation speed using IFOC Figure 8. Rotation speed using DTC
Fuzzy logic direct control, as shown in Figure 9, also offers fast response times by adjusting control
parameters in real-time based on operating conditions. Regarding electromagnetic torque, indirect field-
oriented control, as shown in Figure 10, achieves relatively high precision but may result in undesired torque
fluctuations. Traditional direct control, as shown in Figure 11, typically provides a more stable torque output,
while fuzzy logic direct control, as shown in Figure 12, can effectively suppress torque fluctuations by
adapting control parameters to the operating conditions. In terms of stator flux, indirect field-oriented control,
as shown in Figure 13, is designed to maintain a constant stator flux, ensuring proper motor operation.
Traditional direct control and fuzzy logic direct control, as shown in Figures 14 and 15, also maintain a stable

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stator flux, albeit with some variations due to control parameter adjustments. For stator currents, all control
techniques, as shown in Figures 16, 17, and 18, effectively regulate the currents to meet operational
requirements. However, fuzzy logic direct control demonstrates better suppression of current harmonics by
dynamically adjusting control parameters to minimize disturbances. During startup, indirect field-oriented
control offers a smooth startup, enabling precise control of the flux and torque right from the initial moments.
Nevertheless, it might exhibit longer response times and necessitate a more intricate implementation. On the
other hand, traditional direct control is straightforward and responsive during startup, enabling direct control
of the speed and torque. However, it might be susceptible to fluctuations and instabilities during startup,
potentially requiring supplementary mechanisms. Fuzzy logic direct control adjusts in real-time to operating
conditions, including startup, to ensure a seamless startup process. Nonetheless, its implementation
necessitates accurate system modeling and additional expertise. In terms of the overall comparison of the
three control techniques for an induction machine, fuzzy logic direct control stands out for its fast response
times, ability to suppress torque fluctuations, and adaptability to varying operating conditions. However, each
control technique has its own strengths and limitations, and the most appropriate strategy is determined by
the particular application requirements and performance goals.
Figure 9. Rotation speed using fuzzy logic Figure 10. Magnetic torque using IFOC
Figure 11. Magnetic torque using DTC Figure 12. Magnetic torque using fuzzy logic
Figure 13. Stator flux using IFOC Figure 14. Stator flux using DTC

9
Figure 15. Stator flux using fuzzy logic Figure 16. Stator current using indirect IFOC
Figure 17. Stator current using DTC Figure 18. Stator current using fuzzy logic
4.1. Comparative analysis
Table 3 presents a comparative analysis of the performance of three control methods: indirect flux-
oriented controllers (IFOC), traditional DTC, and fuzzy logic-based DTC. The evaluation is conducted under
both transient and steady-state circumstances, considering key parameters such as rise time, overshoot, and
settling time. The results presented in the table highlight the superior performance of fuzzy logic-based DTC.
Compared to IFOC and traditional DTC controllers, the fuzzy logic-based DTC demonstrates faster rise time,
shorter settling time, and reduced overshoot. This signifies that the fuzzy logic-based approach surpasses
both IFOC and traditional DTC controllers, showcasing the induction motor's remarkable ability to achieve
precise and rapid speed adjustments without experiencing overshoot or inaccuracies in steady-state operation.
To further support these findings, the evolution of the stator current is depicted in Figures 16, 17, and 18.
Notably, the motor utilizing the fuzzy logic-based DTC exhibits an excellent sinusoidal waveform in the
stator current, indicative of well-controlled performance. Conversely, the stator currents in IFOC and
traditional DTC controllers exhibit significant harmonics, suggesting less optimal control. These results
underscore the effectiveness of fuzzy logic-based DTC in achieving superior performance and control
precision for induction motors. The ability to mitigate harmonics and maintain a desirable sinusoidal
waveform in the stator current reinforces the advantages of the fuzzy logic-based approach.
Table 3. Comparative study between proposed strategies
Control strategies Settling time (sec) %Overshoot Rise time (sec)
DTC 0.0295 0.5034 0.0193
DTC based on fuzzy logic 0.01276 0.4173 0.0102
IFOC 0.07312 1.9471 0.0523
5. CONCLUSION
This article presents a novel enhancement to the DTC algorithm for IM by utilizing intelligent
approaches based on fuzzy logic. The proposed method involves replacing the switching selector block and
the two hysteresis controllers with a fuzzy logic controller. Through extensive simulations, it is demonstrated
that the proposed strategy outperforms conventional DTC and field-oriented control techniques. Comparative
analysis between fuzzy logic-based control and other established methods, such as flux-oriented control or
conventional DTC, reveals similar results, further validating the efficiency of fuzzy logic-based
enhancements in the DTC strategy. The fuzzy logic-based algorithm exhibits faster dynamic responses in the

 ISSN: 2088-8708
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transient state and significantly reduces torque ripples in the steady state, regardless of load or no-load
conditions. The key improvements observed in the fuzzy logic-based approach encompass a reduction in
response time, speed rejection time, and flux and torque oscillations, along with a notable reduction in total
harmonic distortion (THD) in the stator current. To advance this research, future work will focus on
implementing the fuzzy logic-based DTC using a field programmable gate arrays-based (FPGA) test platform
within our laboratory.
ACKNOWLEDGEMENTS
This work is supported by the URL 7 grant from the CNRST-Morocco.
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BIOGRAPHIES OF AUTHORS
Radouan Gouaamar obtained his master's degree in renewable energy
engineering, specializing in energy efficiency, network, and electrical energy from the
multidisciplinary faculty of Beni Mellal in Morocco in 2020. In 2017, he received a bachelor's
degree in renewable energy. He is currently a Ph.D. student at Moulay Ismail University in
Meknes, Morocco. His research interests lie in the fields of electrical engineering, power
engineering, and renewable energies. He can be contacted at r.gouaamar@edu.umi.ac.ma.
Seddik Bri is a full professor in the Electrical Engineering Department,
responsible for the Material and Instrumentation Group at the High School of Technology,
Moulay Ismail University, Meknes-Morocco. His research interests are in communication
systems with 200 published conferences. He can be contacted at email: s.bri@umi.ac.ma or
briseddik@gmail.com.
Zineb Mekrini received her Ph.D. degree in electrical engineering from Moulay
Ismail University, Meknes, Morocco, in 2018. She is now a professor at the Electrical
Engineering Department in the Faculty of Sciences and Techniques of Tangier, Abdelmalek-
Essaadi University, Morocco. Her current research includes the control of power converters,
renewable energy, and fuzzy logic control. She can be contacted at zineb.mekrini@gmail.com.

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