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EPE Journal
European Power Electronics and Drives
ISSN: 0939-8368 (Print) 2376-9319 (Online) Journal homepage: http://www.tandfonline.com/loi/tepe20
Control with high performances based DTC
strategy: FPGA implementation and experimental
validation
Saber Krim, Soufien Gdaim, Abdellatif Mtibaa & Mohamed Faouzi Mimouni
To cite this article: Saber Krim, Soufien Gdaim, Abdellatif Mtibaa & Mohamed Faouzi Mimouni
(2018): Control with high performances based DTC strategy: FPGA implementation and
experimental validation, EPE Journal, DOI: 10.1080/09398368.2018.1548802
To link to this article: https://doi.org/10.1080/09398368.2018.1548802
Published online: 27 Nov 2018.
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ARTICLE
Control with high performances based DTC strategy: FPGA implementation
and experimental validation
Saber Krim
a
, Soufien Gdaim
b
, Abdellatif Mtibaa
b
and Mohamed Faouzi Mimouni
a
a
Research Unit of Industrial Systems study and Renewable Energy, National Engineering School of Monastir, University of Monastir,
Monastir, Tunisia;
b
Laboratory of Electronics and Microelectronics, Faculty of Sciences, University of Monastir, Monastir, Tunisia
ABSTRACT
This paper aims to improve the induction motor performances using a modified Direct
Torque Control (DTC). The DTC is featured by its simple structure and fast dynamic response.
Also, it does not require a speed sensor and it is less dependent on the motor parameters.
However, the basic handicaps of this method are the torque ripples and the current distor-
tions due to the presence of the hysteresis controllers. This paper aims; firstly, to replace the
hysteresis controllers and the switching table by a fuzzy logic system in order to overcome
these handicaps. Furthermore, the sampling frequency is another parameter which must be
investigated to reduce the commutation losses and the harmonic stator current waves. The
second purpose of this paper is to reduce the execution time using a Field Programmable
Gate Array (FPGA). The FPGA is able to execute the Fuzzy DTC (FDTC) with a higher sampling
frequency, thanks to its parallel processing relative to the Digital Signal Processor (DSP). The
FDTC model is verified by simulation using system generator tool and validated by experi-
mentation utilizing an FPGA Virtex 5. Thus, excellent performances at low processing time are
obtained using the Xilinx FPGA Virtex 5.
KEYWORDS
Direct torque control; fuzzy
logic system; real time;
Xilinx system generator;
Field Programmable Gate
Array (FPGA)
1. Introduction
Recently, Induction Motors (IMs) are the most used
motors in industrial applications thanks to theirs
simple design, low maintenance, low cost, and it
operates in open loop [1].
Day by day, the basic Direct Torque Control (DTC)
is becoming more popular in the industrial world
thanks to its simple structure, its robustness under
motor parameter variation and its rapid dynamic
response, and because it does not require a speed
sensor [2–4]. Since its invention, the basic DTC is
counted as the most efficient approach for controlling
the induction motor [5]. Despite of the cited advan-
tages, the basic DTC produces a torque and a flux with
high ripples, and a stator current with high distortions,
which affect the stator winding and reduce the motor
service life. The conventional DTC can be improved
utilizing the Space Vector Modulation (SVM) and
discrete space vector modulation techniques, as given
in [6–12]. In this case, it is possible to apply the
required stator voltage vector and guarantee an opera-
tion with constant switching frequency, which conse-
quently reduces the ripples and the commutation
losses. However, in these methods accurate design of
Proportional Integral (PI) controllers for the torque
and the flux is required which affects the system
robustness, the stability and the dynamic of the control
system, due to the variation of the induction motor
parameters and the external disturbances. Others
researchers have associated the DTC-SVM and the
multilevel inverter to control the switching frequency
of the inverter and reduce the ripples [9,13–20]. The
main technologies of the multilevel inverter used in
the industrial applications are the Neutral Point
Clamped (NPC) [17,18], the Flying Capacitors (FC)
[19], and the Cascaded H-Bridge (CHB) [9,20]. Yet,
using the multilevel inverter, switching losses
increases, the reliability decreases and the price of the
system increases. Other studies for the DTC have been
developed, such as the combination between the DTC-
SVM and the predictive control [21], to control the
switching frequency and select the desired voltage
vector. This method provides a significant improve-
ment in terms of ripples. Nevertheless, in this case the
algorithm is based on a complex mathematical model
that depends on several machine parameters. In recent
works a combination between the DTC-SVM and
sliding mode controllers is developed to reduce the
ripples, improve the system robustness and reject per-
turbations [7,22,23]. However, the main problem in
these controllers is the chattering phenomenon, due to
discontinuous nature of the control law which intro-
duces a persistent static error and the need for knowl-
edge of the system dynamics. In addition, the sliding
mode controllers are difficult in design and tuning. In
recent years, the artificial intelligent techniques like the
CONTACT Saber Krim [email protected] Research Unit of Industrial Systems study and Renewable Energy, National Engineering School of
Monastir, University of Monastir, Tunisia
EPE JOURNAL
https://doi.org/10.1080/09398368.2018.1548802
© 2018 European Power Electronics and Drives Association

fuzzy logic and neural networks have gained more
importance to improve the electrical system perfor-
mances. In order to improve the conventional DTC
performances, few researchers have used the fuzzy
logic and sliding mode fuzzy to improve the conven-
tional DTC performances by replacing the PI control-
ler by an intelligent controller, as given in [24,25]. In
these works, the speed transient response is improved
but the torque ripples are not much reduced.
Improvements in the DTC performances using neural
networks based switching controller is developed in
some researches works, as given in [26]. The drawback
of Artificial Neural Network (ANN) based switching
controller is that it doesn’t give heuristic knowledge of
process in selection of optimal switching voltage vec-
tor; in addition the control algorithm is complex and
requires a processor with high computation power. To
overcome the limitations of all the cited control stra-
tegies, the fuzzy logic can be used to improve the
nonlinear system performances. The fuzzy logic pro-
cess is easy to understand due to its simple logical
structure and inference mechanism. In fact, the con-
ventional DTC uses two hysteresis controllers to con-
trol the torque and the stator flux. These controllers
generate two states, which produce the same results for
the small and the big error of the torque and the stator
flux. As consequence, the generated switching states of
the inverter cannot produce the best voltages vectors
to make the flux and the torque errors both zero. This
leads in ripples of torque and flux [27–29], and varia-
tions of the switching frequency [30]. In paper [31],
the authors present an analysis of the switching fre-
quency variation which is caused by the hysteresis
band, the motor speed and torque slope. These ripples
can be reduced if the torque and flux errors are sub-
divided into several smaller subsections on which dif-
ferent control action is taken. However, it is hard to
obtain the optimal subsection boundaries for the tor-
que and flux errors. Furthermore, the torque ripples
are affected by the width of the torque hysteresis band
[32]. These ripples change proportionally with the
variations in hysteresis bandwidth of the torque.
However, due to the discrete nature of the control
system, there might be still ripples even with the zero
bandwidth of the hysteresis controller. In addition, if
the bandwidth decreases, the inverter switching fre-
quency increases, which consequently increases the
switching losses in the semiconductors. To solve this
problem the bandwidth of the torque hysteresis con-
troller must be optimized in order to keep the ripple
level and switching frequency variations within accep-
table limits. Therefore, the Fuzzy Logic Control (FLC)
based on the language rules can be used to adapt the
torque hysteresis band in order to reduce the torque
ripples and provide a precise speed response by select-
ing the suitable voltage vector [33–35]. The Fuzzy logic
uses the people’s experience knowledge to release the
rule base and it is based on intuition and simulation
[36]. The FLC is not based on the IM model and its
parameters. It is more suitable for the imprecise pro-
cesses and does not require any mathematical models.
Also, the FLS is a more effective technique when the
system is based on a severely nonlinear mathematical
model [37]. The FLC has been proposed in several
researches work to select the optimal voltage vectors
in conventional DTC [38–41] and to solve the problem
of variable switching frequency [42,43]. Other
researchers have used the fuzzy logic in speed control
andflux observer to improve the electrical motors
performances [44–47]. In addition, the fuzzy logic is
used in some research works to improve the DTC
performances and to reduce the commutation losses
in the inverter, as given in [48–50]. In paper [48], the
authors presented a fuzzy logic controller associated to
the DTC of PMSM to reduce the ripples and the
switching losses in the inverter by arranging the inver-
ter switching. In paper [49,50], the authors present
a combination between the DTC and the fuzzy logic
control in order to provide an operation with low
ripples and to keep the switching losses at an accep-
table level, by selecting a suitable voltage vector for
each sampling period. This demonstrates that utilizing
the fuzzy logic technique, it is possible to improve the
DTC performances by reducing the ripples of the
inverter losses.
The first contribution in this paper is oriented
toward the use of a Fuzzy Logic System (FLS) to replace
the switching table and the hysteresis controllers, in
order to reduce the torque and the flux ripples, as well
as the stator current distortions. The design with the
XSG tool and the hardware implementation on the
FPGA of the proposed Fuzzy DTC (FDTC) control
strategy is the second contribution in this paper. In
this study, the torque and the flux errors and the stator
flux vector position are fuzzified into several fuzzy sub-
sets in order to select a more suitable space voltage
vector to smooth the torque and the flux ripples and
control the switching frequency variations. With the
proposed method, the transistors are only switched
when it is needed which consequently reduces the
inverter switching losses. The Fuzzy rule base provides
precise control by selecting optimal switching state of
the inverter in order to meet required torque and flux
demand by the induction motor. The performance of
the proposed method has been verified by digital simu-
lation utilizing the Matlab/Simulink environment and
the Xilinx System Generator (XSG) tool, and experi-
mental validation using an FPGA Virtex 5 and
a squirrel cage induction motor.
Traditionally, two main families of digital devices
are used to control the induction motor drive. The
first family integrates the microcontroller (STM32F3,
STM32F4, STM32F1. . .) [51,52] and the Digital
Signal Processor (DSP) [53–55] which can be
2S. KRIM ET AL.

programmed with ‘C code’or a graphical program-
ming approach MATLAB/Simulink [56]. The second
family integrates the Field Programmable Gate Array
(FPGA) device (FPGA) [57] which can be pro-
grammed with VHSIC Hardware Description
Language (VHDL) or Verilog code. However, the
limitation factor of the STM microcontrollers is the
computing power and the processing speed which
depends of the algorithm complexity, this limit allows
fixing an important sampling period which creates
delays in the control loop. The DSPs controllers’
devices are considered as an appropriate solution in
electrical drives applications [58] which integrate
a high-performing processor core based on
a hardware accelerator computing block and few
peripherals to communicate with the external envir-
onment. It should be noted that the sampling fre-
quency of the processor depends on the
computational burden due to the serial processing,
which consequently creates delays in the feedback
loop and reduces the control algorithm performances.
In fact, if the sampling time increased, the torque and
flux ripples and the stator current harmonic wave-
forms increased [59,60]. For the conventional DTC
algorithm, generally the sampling frequency of the
DSP (DSPACE 1104 based on a DSPTMS320F240)
can reach up to 20 kHz [61]. However, this sampling
frequency is still insufficient for the hysteresis con-
trollers operations to reach the same performances
provided by the analogue control solutions, such as
the accuracy and the absence of the delays in the
feedback loop. To overcome the DSP limitation,
a combination between the DSP and the FPGA has
been developed by some researchers to reduce the
calculation burden of the DSP, by distributing some
algorithm tasks to the FPGA [62,63], thus the sam-
pling period is minimized and the ripples are
reduced. However, this is not a solution for commer-
cialization, due to the complexity of the interfacing
circuits and the high cost. To overcome these limita-
tions, the FPGA can be chosen as an alternative
solution to control the induction motor with shorter
execution time, which can be beneficial in high per-
formance applications. In this paper, the DTC based
on a FLS, referred to as Fuzzy-DTC (FDTC) can be
considered as a complex control algorithm that
requires a digital device with a high computation
power. Recently, several researchers have used the
software solutions like the DSP (e.g. dSPACE 1104)
to control the electrical motor [61,64,65]. However,
the main limitation factor of these solutions is that
the processing speed depends on the complexity of
the algorithm by adopting a serial processing [57]. In
this context, other researchers have explored utilizing
the FPGA to overcome the DSP limitation [60,66–
68]. In this work, the FPGA is chosen, thanks to its
parallel processing. Thus, using the FPGA it is
possible to implement more complex algorithms
with a low execution time and a smaller sampling
period which can reach up to 200 kHz [68].
Configuring an FPGA, a bitstream file is required,
which can be generated using a VHDL or Verilog
code. On the other hand, programming the VHDL
or Verilog is difficult and requires a lot of prototyping
time. To overcome this problem, a Xilinx System
Generator (XSG) toolbox is used in this work to auto-
matically generate the VHDL (or Verilog) code and
the bitstream file [69,70]. The XSG is a toolbox inte-
grated into a Simulink environment, developed by
Xilinx and featured by its simplicity and rapid imple-
mentation time [71]. In this paper, the DTC based on
the fuzzy logic control has been developed, designed
from the XSG tool, verified by digital simulation and
experimental validation utilizing an FPGA Virtex 5.
The rest of the paper is structured as follows: The
basic DTC principle is detailed in section 2. The pro-
posed FDTC is developed in section 3. In section 4, the
design, the digital simulation, and hardware implemen-
tation on the FPGA of the basic DTC and the FDTC are
presented. A comparative study between the basic DTC
and the FDTC is shown in section 5 by experimental
results. Finally, a conclusion is presented in section 6.
2. Basic DTC principle
In the Concordia reference (α,β), the IM model can
be described as follows:
dφsα
dt ¼vsαRsisα
dφsβ
dt ¼vsβRsisβ
dφrα
dt ¼Rrirαωφrβ
dφrβ
dt ¼Rrirβþωφrα
8
>
>
>
>
>
>
<
>
>
>
>
>
>
:
(1)
where
●ðvsα;vsβÞ;ðφsα;φsβÞ;ðφrα;φrβÞand ðisα;isβÞ: are
the components of the voltage, the stator flux,
the rotor flux and stator current respectively, in
the Concordia reference (α,β). Rs;Rrand ω: are
the stator resistance, the rotor resistance and the
motor speed.
The mechanical motor behavior is described as
follows:
JdΩ
dt ¼TeTlfΩ
Tem ¼3
2Npφsαisβφsβisα
((2)
where
●Teand Tlare the electromagnetic torque and
the load one. J;fandN
pare rotor inertia,
JOURNAL: EUROPEAN POWER ELECTRONICS AND DRIVES 3

viscous friction coefficient, and the number of
pole pairs.
The basic idea of the DTC method is to control the
stator flux and the electromagnetic torque simulta-
neously through the selected voltage vector for each
sampling time. The basic DTC diagram is given in
Figure 1 [2,4,5].
where the components φsαand φsβare represented
by the following equations:
φsα¼ðvsαRsisα
ðÞdt
φsβ¼ðvsβRsisβ
dt
8
>
<
>
:
(3)
The components ðvsα;vsβÞare expressed as:
According to Figure 1, the torque and the flux are
both controlled by two hysteresis controllers. Indeed,
the torque and flux errors present the hysteresis con-
troller inputs. These controllers present in their out-
puts two logical decisions ðKφand KTÞ.
To determine the position where the stator flux
vector is located, the following equation can be used:
θs¼arctg φsβ
φsα
 (4)
The basic DTC consists in subdividing the Concordia
(α,β) reference in six sectors (sector 1 to sector 6);
each sector is defined by an angle of 60° as given by
Figure 2 [2,4]. The different sectors and voltage-
vector positions are illustrated in Figure 2.
As presented in Figure 1, the voltage vector is
selected through the hysteresis controller’s decisions
ðKφ;KTÞand the position of the stator flux vector
(N), utilizing the switching Table [4].
3. Proposed approach: fuzzy direct torque
control (FDTC)
3.1. FDTC principle
The real problem of the basic DTC is that the torque
and the flux are known by high ripples because of the
poor choice of a voltage vector for each sampling
time. These ripples are caused by the hysteresis con-
trollers and the switching table, which are replaced by
a fuzzy approach. The fuzzy logic is able to generate
the desired voltage vector, which consequently
reduces the ripples. The FDTC diagram of an induc-
tion motor is illustrated in Figure 3.
In order to get a control with high performances,
a Fuzzy Logic System (FLS) is used to replace the hyster-
esis controllers and the switching table, as illustrated in
Figure 3. The FLS has three inputs and one output. The
fluxerror,thetorqueerrorandtheangleθS(stator flux
vector position) are the FLS inputs. The voltage vector V
i
is the FLS output. The FLS is based on three steps [72]:
i) the fuzzification step consists in to converting input
analogue variables into fuzzy variables, ii)abaseofrules
whichisbasedonthefuzzyrulesanddescribesthe
functionality of the fuzzy system—in this step we have
found also a fuzzy inference engine which associates the
inputs variables with fuzzy rules, iii)thedefuzzification is
Figure 1. Block diagram of the basic DTC.
4S. KRIM ET AL.