In this paper the hardware implementation of the direct torque control based on the fuzzy logic technique of induction motor on the FieldProgrammable Gate Array (FPGA) is presented. Due to its complexity, the fuzzy logic technique implemented on a digital system like the DSP (Digital Signal Processor) and microcontroller is characterized by a calculating delay. This delay is due to the processing speed which depends on the system complexity. The limitation of these solutions is inevitable. To solve this problem, an alternative digital solution is used, based on the FPGA, which is characterized by a fast processing speed, to take the advantage of the performances of the fuzzy logic technique in spite of its complex computation. The Conventional Direct Torque Control (CDTC) of the induction machine faces problems, like the high stator flux, electromagnetic torque ripples, and stator current distortions. To overcome the CDTC problems many methods are used such as the space vector modulation which is sensitive to the parameters variations of the machine, the increase in the switches inverter number which increases the cost of the inverter, and the artificial intelligence. In this paper an intelligent technique based on the fuzzy logic is used because it is allows controlling the systems without knowing the mathematical model. Also, we use a new method based on the Xilinx system generator for the hardware implementation of Direct Torque Fuzzy Control (DTFC) on the FPGA. The simulation results of the DTFC are compared to those of the CDTC. The comparison results illustrate the reduction in the torque and stator flux ripples of the DTFC and show the Xilinx Virtex V FPGA performances in terms of execution time.
1. Introduction
In the last few years, the most digital implementations of control algorithms of electric machines are based on software solutions such as microcontrollers and digital signal processor. However, these solutions present some disadvantages; for example, the used sampling period is limited by the time of computation. To overcome the traditional software solution limitations, new hardware solutions such as the FPGAs can be used, which present the appropriate digital solutions for the implementation of control algorithms. The inherent parallelism of these new digital solutions as well as their large computing capacity, making computation time delays, are negligible despite the complexity of the algorithms to implement.
The conventional direct torque control of the induction motor is characterized by outstanding dynamic performances as well as good robustness to against changes of motor parameters. However, the Conventional Direct Torque Control (CDTC) also has some drawbacks, like high electromagnetic torque and stator flux ripples and high stator current distortion
[1

3]
. To improve the CDTC performances, many methods are used, such as the fuzzy logic control, the Space Vector Modulation (SVM)
[4

6]
, and the increase in the switches inverter number
[7]
. Nevertheless, the use of SVM needs several motor parameters and increases the complexity of the Direct Torque Control (DTC) algorithm. Moreover, the rise in the switches inverter number increases costs. In this work, our orientation is focused to the fuzzy logic control, because it is an effective technique used where mathematical model is complicated or the mathematical model does not exist, as well it is allows controlling the systems without knowing the mathematical model. The objective of this work is to implement the Direct Torque Fuzzy Control (DTFC) of the induction motor on the FPGA in order to take advantage of these performances in the field of digital control of electrical machines in real time. The Fuzzy Logic Control (FLC) is used to replace the switching table, the sector block and the two hysteresis comparators
[8]
.
During the last few years several researchers use the hardware implementation on the FPGA for controlling electrical system
[9

15]
. Most of them use the VHDL (VHSIC hardware description language). In this study, the Xilinx System Generator (XSG) is used to automatically generate the VHDL code. The advantages of this method are the rapid time to market, real time, and portability.
2. Contributions of Xilinx System Generator
The XSG is a modeling tool developed by the Xilinx to design implemented systems on the FPGA. It has a library of varied blocks, which can be automatically compiled into an FPGA
[16]
. In this work, the (XSG) is used to implement the DTFC architecture of the induction motor based on Fuzzy Logic Control (FLC) algorithm on an FPGA. In the first step, we begin by implementing the proposed architectures using the XSG blocks available on the Simulink library. Once the Design of the system is completed and gives the desired simulation results, the VHDL code can be generated by the XSG tool
[17]
. The design flow of the XSG is given in
Fig. 1
. After generating the VHDL code and the synthesis, we can generate the bitstream file. Then we can move this configuration file to program the FPGA
[18]
.
Xilinx system generator design flow
3. Basic CDTC Principle of Induction Motor
 3.1 Induction machine model
The model of the induction motor expressed in the stationary “αβ” axes reference frame can be described by
[19]
:
where, i
_{S}
, φ
_{S}
, V
_{S}
, R, and L denote the stator currents, stator flux, stator voltage, resistance, and inductance, respectively, and where ω denotes the rotor speed and
is the redefined leakage inductance.
The electromagnetic torque of the induction motor can be expressed in terms of stator currents and stator flux, which is given by the following expression:
The mechanical speed equation is given by the following equation:
where p is the number of pole pairs, J and T
_{L}
denote the moment of inertia of the motor and the load torque, Ω is the rotor mechanical speed (pΩ=ω), and f is a viscous friction coefficient.
 3.2 CDTC Principle
The basic principle of the CDTC is based on the application of a voltage particular sequence via a voltage inverter, whose waves are generated through hysteresis comparators in which the flux and torque are trapped to follow their references
[20]
. The components of the stator voltage vector in the stationary reference frame are calculated as follows:
where V
_{dc}
is the DC bus voltage.
The components of the stator current vector are given by the following expression:
The module of the stator flux is given by Eq. 6:
The angle between the stator flux
ϕ_{s}
(
ϕ_{sα}
,
ϕ_{sβ}
) and the reference axis is given by Eq. (7):
The estimated values of the torque and stator flux are compared to the reference values Te
^{*}
and φ
_{S}
^{*}
, respectively. It can be seen from
Fig. 3
that the error between the estimated torque Te and the reference torque Te
^{*}
is the input of a three level hysteresis comparator, where the error between the estimated stator flux magnitude φ
_{S}
and the reference stator flux magnitude φ
_{S}
^{*}
is the input of a two level hysteresis comparator.
Basic direct torque control scheme
Finally, the outputs of the comparators with a stator flux sector, where the stator flux space vector is located, select an appropriate inverter voltage vector from the switching table (
Table 1
). The selected voltage vector will be applied to the induction motor at the end of the sample time
[21]
.
Switching table for conventional DTC
Switching table for conventional DTC
The vectors V
_{1}
,...,V
_{6}
represent the six active vectors that can be generated by a voltage source inverter where V
_{0}
and V
_{7}
are the two zero voltage vectors.
Fig. 2
gives the partition of the complex plan in six angular sectors S
_{i}
=1...6.
Partition of the complex plan in six angular sectors
When the stator flux is in zone i, the vector V
_{i+1}
or V
_{i−1}
is selected to increase the level of the flux, and V
_{i+2}
or V
_{i−2}
is selected to decrease it. At the same time, the vector V
_{i+1}
or V
_{i−2}
is selected to increase the level of the electromagnetic torque, and V
_{i−1}
or V
_{i−2}
is selected to decrease it.
If V
_{0}
or V
_{7}
is selected, the rotation of the stator flux is stopped and the torque decreases, whereas the amplitude of the stator flux remains unchanged. This shows that the choice of the vector tension depends on the sign of the error of the stator flux and electromagnetic torque independently from their amplitude
[21]
. This explains why the output of the hysteresis comparator of the stator flux and torque must be a Boolean variable. We can add a band of hysteresis around zero to avoid useless commutations when the error of the stator flux is very small
[21]
.
With this type of hysteresis comparator, we can easily control and maintain the end of the vector flux within a circular ring.
The basic structure of the CDTC induction motor is shown in
Fig. 3
.
4. DTC Based On Fuzzy Logic Control
The fuzzy approach is proposed to reduce the torque, flux and current ripples. The fuzzy logic controller has been introduced to replace the two hysteresis controllers and the switching table.
Fig. 4
illustrates the structure of the fuzzy DTC of induction motor. The inputs of the fuzzy controller bloc are the torque error, flux error and stator flux angle. The voltage vector is the output.
Fuzzy direct torque control (DTCF) scheme
 4.1 Fuzzy logic controller principle
In the fuzzy logic controller system, we can distinguish three main parts: fuzzification, fuzzy reasoning and defuzzification
[22]
. The fuzzification is based on the membership functions.
 4.2 Fuzzy variables
The membership function of the stator flux error is given by three linguistic terms: Negative (N), Zero (Z) and Positive (P), as shown in
Fig. 5(a)
. The error of the stator flux is given by the following equation:
(a) Fuzzy membership functions of the flux error ( e_{ϕ}); (b) Fuzzy membership functions of the torque error e_{T}
The membership function of the electromagnetic torque error is given by five linguistic terms: Negative Large (NL), Negative Small (NS), ZEro (ZE), Positive Small (PS) and Positive Large (PL) and shown in
Fig. 5(b)
. The error of the electromagnetic torque is given by the following equation:
The membership function of the angle
θ_{s}
is given by twelve fuzzy sets:
θ
_{1}
,
θ
_{2}
,
θ
_{3}
,
θ
_{4}
,
θ
_{5}
,
θ
_{6}
,
θ
_{7}
,
θ
_{8}
,
θ
_{9}
,
θ
_{10}
,
θ
_{11}
, and
θ
_{12}
as shown in
Fig. 6
.
Stator flux position membership function
The membership functions of the output space voltage vectors are shown in
Fig. 7
.
Fuzzy membership functions of output
A system with three inputs, one having three fuzzy sets, another having five fuzzy sets, and the other having twelve fuzzy sets, has a total of 3*5*12=180 different control rules shown in
Table 2
.
Fuzzy control rule table
Each control rule can be described using the three input variables: the stator flux error
e_{f}
, the electromagnetic torque error
e_{T}
and the angle
θ_{s}
and the output variable V as:
where Xi, Yi and Zi are the fuzzy sets
e_{f}
,
e_{T}
and θ,
 4.3 Fuzzy inferences
In this work we use the inference method of Mamdani’s which is based on the minmax decision.
There are several methods of defuzzification, such as the method of the center of gravity and the maximum method. In our case, we have used the latter method.
Fig. 8
shows the concrete reasoning of the fuzzy inference system.
Steps of the fuzzy logic system for the DTFC technique
 4.4 Design of membership functions using the xilinx system generator
The fuzzy logic controller bloc propagates the inputs (sator flux error
e_{f}
, electromagnetic torque error
e_{T}
, and angle
θ_{s}
) to the output voltage vectors Vi. The structure of the fuzzy logic controller bloc is shown in
Fig. 9
.
Fuzzy logic controller bloc from the Xilinx System Generator
The stator flux error is given by three fuzzy sets; evenly, we have three mathematical functions, named: P, Z and N. The membership function of N is given by Eq. 11 and illustrated using the XSG, as in
Fig. 10
.
Design of the membership function N of the stator flux error from the XSG
The electromagnetic torque error is given by five fuzzy sets; evenly, we have five mathematical functions, named: NL, NS, ZE, PS and PL. The membership function of ZE is given by Eq. 12 and illustrated using the XSG, as in
Fig. 11
.
Design of the membership function ZE of the electromagnetic torque error using the XSG
The angle
θ_{s}
is given by twelve fuzzy sets:
θ
_{1}
,
θ
_{2}
,
θ
_{3}
,
θ
_{4}
,
θ
_{5}
,
θ
_{6}
,
θ
_{7}
,
θ
_{8}
,
θ
_{9}
,
θ
_{10}
,
θ
_{11}
, and
θ
_{12}
.
The membership function of
θ
_{4}
is given by Eq. 13 and illustrated using the XSG, as in
Fig. 12
.
Design of the membership function θ_{4} of θ_{s} error using the XSG
 4.5 Rules design
In this work we use the inference method of Mamdani’s which is based on the minmax decision. The output of each fuzzy rule is given by the minimum function between the three membership functions of the stator flux error Δ
φ
, the electromagnetic torque error Δ
T_{e}
, and the angle
θ_{S}
. The design of rule number 26 using the XSG is given by
Fig. 13
.
R26: If (
e_{f}
is N) & (
e_{T}
is PL) & (
θ_{s}
is
θ
_{5}
), then (V is V1)
 4.6 Rules interconnection
To move from one rule to another, the OR operator is used. This operator is presented by the Maximum function and connects all rules. The output presents the maximum value between the inputs. The design of the Max block for the rules with the output voltage vector Vi is given by
Fig. 14
.
Design of the maximum block connecting the V1 output rules
 4.7 Control vector block
For each sampling period, three Boolean variables (Sa, Sb, Sc) are determined for each selected voltage vector Vi. The control vector block is illustrated using the XSG, as in
Fig. 15
.
Calculation of sequences (S_{a}, S_{b}, S_{c}) using the voltage vector
5. Simulation Result and Discussion
The simulation of the CDTC and the DTFC of the induction motor is achieved using the XSG. The flux reference is 0.91 (wb), and the system period is 50µs. The reference of the electromagnetic torque is the output of the proportional integral controller of the rotor speed. The load torque is applied at the times t=0.3sec and t=0.6sec. For the two CDTC and DTFC techniques we obtain the evolution of the rotor speed, the stator flux, the electromagnetic torque, the stator current module, and the sinusoidal distribution of the stator current, which are given by
Fig. 16

19
, and
Fig. 20
, respectively.
Evolution of the rotor speed: (a) CDTC; (b) DTFC
Evolution of the stator flux: (a) CDTC; (b) DTFC
Evolution of the electromagnetic torque: (a) CDTC; (b) DTFC
Evolution of the stator current module: (a) CDTC; (b) DTFC
Evolution of the stator current: (a) CDTC; (b) DTFC
In
Fig. 16
, the rotor speed has quickly reached its reference value, 150 rd/s, which shows the high dynamics of the DTC.
In
Fig. 17
, the stator flux has quickly attained its reference value, 0.91 wb, which shows the high dynamics of the DTC. The stator flux in the case of the CDTC is characterized by high ripples, which has decreased by using the fuzzy logic.
The evolution of the electromagnetic torque is given by
Fig. 18
. We can notice that the electromagnetic torque ripples is reduced in the case of the DTFC.
In
Fig. 19
, it can be seen that the stator current ripples is reduced by using the FLC. In
Fig. 20
, we can notice that the stator current distortion is reduced in the case of the DTFC.
From the simulation results we can have the
Table 3
to compare the results observed in the case of the CDTC and the DTFC.
Advantages of DTFC in terms of ripples
Advantages of DTFC in terms of ripples
6. DTFC Implementation on FPGA
From the DTFC design, using the XSG, we can automatically generate the VHSIC Hardware Description Language (VHDL) code to be implemented into the FPGA. The implementation results of the CDTC and the DTFC are given in
Fig. 21
which shows the RTL (Register Transfer Level) schematic of the DTFC in the Xilinx ISE 12.4 and given in the
Table 4
that presents the used resources.
Synthesis result of the DTFC using the Xilinx ISE 12.4
Used resources
The performance of the hardware implementation on FPGA in term of processing speed is shown in
Table 5
.
FPGA performance in terms of computing time
FPGA performance in terms of computing time
To control the industrial systems like the induction motor, two main families of digital devices such as the microcontroller, the Digital Signal Processor (DSP) and the FPGAs. The first family is based on the microcontroller and the DSP controllers. These components integrate a microprocessor and several peripherals used for the communication with the industrial environment. The generalpurpose microcontrollers include a 16bit or 32bit Reduced Instruction Set Computer (RISC) core and different peripherals, such as the STM microcontrollers (STM32F3, STM32F4, STM32F1…)
[23
,
24]
. The main limitation factor of the general purpose microcontroller is the computing power. This limit allows fixing the sampling period. The DSP controllers integrate a high performing processor core based on a hardware accelerator computing block and few peripherals. In the field of the control of the induction motor, the digital signal processor dSPACE (dSPACE 1102(DSP TMS320), dSPACE 1103(DSP TMS 320 F240), and dSPACE 1104 (DSP TMS320 F240)) is so much used, but the main limitations of this solution is the low processing speed due to the sequential processing
[25

27]
compared to the FPGA. The programming language of these solutions is the ‘C’ code
[28]
, but in most cases the Matlab/Simulink is used to program these circuits
[25
,
32]
. Generalpurpose microcontrollers are characterized by their low price
[29]
. But are not recommended for the complex algorithms due to its low processing speed relative to the FPGA and dSPACE. In
Table 4
, it is clear that the used resources are negligible relative to the available resources. Therefore, in the industrialization phase we can choose an FPGA with a few resources and a low price. In
[30]
The FPGA is inexpensive relative to the dSPACE, and it can execute in real time, with a very low execution time, a full complex algorithm, this due to its processing parallelisms. Therefore, the cost of the system based on the FPGA is lower compared to that based on the dSPACE.
The performances of the hardware solution based on the FPGA compared to software solutions (generalpurpose microcontroller (STM 32F4…), Digital Signal Processor dSPACE 1104), is shown in
Fig. 22
.
Timing diagram for the implementation on: (a) Generalpurpose microcontroller (STM 32F4…); (b) Digital Signal Processor and Control Engineering (dSPACE 1104); (c) Xilinx VirtexV FPGA with an xc5vfx70t3ff1136 package
The computing power of the dSPACE 1104 is greater than that of the STM32 microcontroller due to their adapted architecture. The papers
[31

34]
present the digital control of the induction motor using the dSPACE 1104. In these papers, the sampling time is 100µs, due to the sequential processing of the dSPACE. In paper
[25]
, the execution time is of 300µs using the dSPACE 1102. Using the FPGA, the execution time of the control algorithm of the induction motor is ( ≈ 1 to 2µs). Therefore, the obtained execution time using the FPGA is far lower compared to the programmed solution. In this paper, the execution time of the DTFC algorithm is of 0.89µs, using the Xilinx VirtexV FPGA with an xc5vfx70t3ff1136 package.
7. Conclusion
In this paper, an intelligent technique based on the Fuzzy Logic Control is used to improve of the performances of the Conventional Direct Torque Control of the induction motor. In the Direct Torque Fuzzy Control the switching table, the sector block, and the hysteresis comparators of the stator flux and electromagnetic torque are replaced by the intelligent technique. The main improvements shown are the reduction in the stator flux, the electromagnetic torque ripples and the stator current distortions. Comparatively, the execution time is very weak.
This Direct Torque Control with the Fuzzy Logic Control is designed to be implemented on the Field Programmable Gate Array (Xilinx VirtexV FPGA) using the Xilinx System Generator, which presents an interesting approach. Its use is very practical since the HDL knowledge is unnecessary and so the implementation time is reduced. Thus, the obtained design can be translated automatically into a VHDL programming language and can be embedded into the Xilinx VirtexV FPGA application board.
BIO
Saber Krim received the degree in Electrical Engineering from National School of Engineering of Monastir, Tunisia in 2011. In 2013 he received his M.S degree in electrical Engineering from Monastir University, Tunisia. He is currently pursuing the Ph.D. degree with University of Monastir, Tunisia. His current research interests include rapid prototyping and reconfigurable architecture for realtime control applications of electrical system.
Soufien Gdaim received the degree in Electrical Engineering from National School of Engineering of Sfax, Tunisia in 1998. In 2007 he received his M.S degree in electronic and realtime informatic from Sousse University and received his PhD degree in Electrical Engineering in 2013 from ENIM, Tunisia. His current research interests include rapid prototyping and reconfigurable architecture for realtime control applications of electrical system.
Abdellatif Mtibaa is currently Professor in MicroElectronics and Hardware Design with Electrical Department at the National School of Engineering of Monastir and Head of Circuits Systems Reconfigurable ENIMGroup at Electronic and microelectronic Laboratory. He holds a Diploma in Electrical Engineering in 1985 and received his Ph.D degree in Electrical Engineering in 2000. His current research interests include System on Programmable Chip, high level synthesis, rapid prototyping and reconfigurable architecture for realtime multimedia applications. Dr. Abdellatif Mtibaa has authored/coauthored over 100 papers in international journals and conferences. He served on the technical program committees for several international conferences. He also served as a coorganizer of several international conferences.
Mohamed Faouzi Mimouni received his Mastery of Science and DEA from ENSET, Tunisia in 1984 and 1986, respectively. In 1997, he obtained his Doctorate Degree in Electrical Engineering from ENSET, Tunisia. He is currently Full Professor of Electrical Engineering with Electrical Department at the National School of Engineering of Monastir. His specific research interests are in the area Power Electronics, Motor Drives, Solar and Wind Power generation. Dr. Med Faouzi MIMOUNI has authored/coauthored over 100 papers in international journals and conferences. He served on the technical program committees for several international conferences.
Chintan Patel
,
Rajeevan P. P
,
Anubrata Dey
,
Rijil Ramchand
,
Gopakumar K.
2012
“Fast Direct Torque Control of Openend Induction Motor Drive using 12sided Polygonal Voltage Space Vectors,”
IEEE Transactions on Power Electronics
27
(1)
400 
410
DOI : 10.1109/TPEL.2011.2159516
Monmasson Eric
,
Cirstea Marcian N.
2007
“FPGA Design Methodology for Industrial Control Systems — A Review,”
IEEE transactions on industrial electronics
54
(4)
1824 
1842
DOI : 10.1109/TIE.2007.898281
Zhang Yongchang
,
Zhu Jianguo
,
Zhao Zhengming
,
Xu Wei
,
Dorrell David G.
2012
“An Improved Direct Torque Control for ThreeLevel InverterFed Induction Motor Sensorless Drive,”
IEEE transactions on power electronics
27
(3)
1502 
1513
DOI : 10.1109/TPEL.2010.2043543
Mengjia J
,
Jianqi Q
,
Cenwei S
,
Ruiguang L
A fuzzy DTC method with a SVM defuzzification to Permanent Magnet synchronous Machine
The 30th annual conference of the IEEE inductrial electronics society
Korea
November 25, 2004
Saurabh N
,
Pandya A
,
Chatterjee J
Torque Ripple Minimisation in direct torque control based IM drive
2008 IEEE
Part 1: Singlerate control strategy. 9781424417629/08
Yuttana K
,
Suuttichai P
,
Hamid A
2008
Modified Direct Torque Control method for induction motor drives based on amplitude and angle control of stator flux
Elsevier Publisher
Toh C
,
Idris N
,
Yatim A
,
Muhamad N
,
Elbuluk M
2005
Implementation of a New Torque and Flux Controllers for Direct Torque Control (DTC) of Induction Machine Utilizing Digital Signal processor (DSP) and Field Programmable Gate Arrays (FPGA)
Power Electronics Specialists Conference, IEEE 36th 16
16 June 2005
Rajesh K.
,
Gupta R.
,
Bhangale S.
,
Himanshu G.
2008
— Artificial Neural Network Based Direct Torque Control of Induction Motor Drives
IETECH Journal of Electrical Analysis
IETECH Publications
2
Hasanien H. M.
2011
FPGA implementation of adaptative ANN controller for speed regulation of permanent stepper motor drives
Energy Conversion and Management
Elsevier Publisher
Monmasson E.
,
Idkhajine L.
,
Cirstea M. N.
,
Bahri I.
,
Tisan A.
,
Naouar M. W.
2011
FPGAs in Industrial Control Applications
IEEE Transactions on Industrial Informatics
7
(2)
224 
243
DOI : 10.1109/TII.2011.2123908
Dagbagi M.
,
Idkhajine L.
,
Monmasson E.
,
SlamaBelkhodja I.
2012
FPGA Implementation of Power Electronic Converter RealTime Model
International Symposium on Power Electronics, Electrical Drives, Automation and Motion
658 
663
MONMASSON E.
,
BAHRI I.
,
IDKHAJINE L.
,
MAALOUF A.
,
NAOUAR M. W.
2012
Recent Advancements in FPGAbased controllers for AC Drives Applications
IEEE
13th International conference on Optimization of Electrical and Electronic Equipment (OPTIM)
8 
15
Shahbazi M.
,
Poure P.
,
Saadate S.
,
Zolghadri M. R.
2013
FPGABased Reconfigurable Control for Fault Tolerant BacktoBack Converter without redundancy
IEEE Transactions on Industrial Electronics
60
(8)
3360 
3371
Jezernik K.
,
Korelic J.
,
Horvat R.
2013
PMSM Sliding Mode FPGABased Control for Torque Ripple Reduction
IEEE Transactions on Power Electronics
28
(7)
3549 
3556
DOI : 10.1109/TPEL.2012.2222675
Sutikno T.
,
Idris N. R.
,
Jidin A.
,
Cirstea M. N.
2013
An Improved FPGA Implementation of Direct Torque Control for Induction Machines
IEEE transactions on Industrial Informatics
9
(3)
1272 
1279
DOI : 10.1109/TII.2012.2222037
1998
Xilinx system generator v2.1 basic tutorial
http://bwrcs.eecs.berkeley.edu/Classes/cs152/handouts/Tutorials_book.pdf
Printed in USA
Mailloux J.G.
2008
Prototypage Rapide de la Commande Vectorielle sur FPGA à l'Aide des Outils SIMULINKSYSTEM GENERATOR
l’Université de Québec
2008
White paper: Using System Generator for Systematic HDL Design, Verification, and Validation WP283 (v1.0)
Belkacem S.
,
Naceri F.
,
Abdessemed R.
2010
“Robust nonlinear control for direct torque control of induction motor drive using space vector modulation”
Journal of Electrical Engineering
10
79 
87
Schonfield J. R G
«Direct torque controlDTC»
ABB Industrial Systems Ltd
Takahashi I
,
Noguchi T
1986
A new quickresponse and high efficiency control strategy of an induction motor
IEEE trans
IA22
(5)
Naaz1 S.
,
Afshar A.
,
Ranjit B.
2011
“Effect of different defuzzification methods in a fuzzy based load balancing application”
IJCSI International Journal of Computer Science
8
(5)
261 
267
Motor control with STM32®32bit ARM®based MCU. Pdf
http://www.st.com/web/en/resource/sales_and_marketing/promotional_material/brochure/brstm32mc.pdf
STM32 embedded target for MATLAB and Simulink
http://www.st.com/stwebui/static/active/en/resource/technical/document/data_brief/DM00080897.pdf
Boussak M.
,
Jarray K.
2006
“A HighPerformance Sensorless Indirect Stator Flux Orientation Control of Induction Motor Drive”
IEEE Transactions on industrial electronics
53
(1)
14 
49
DOI : 10.1109/TED.2005.860630
Hmidet A.
,
Dhifaoui R.
,
Hasnaoui O.
2010
“Development, Implementation and Experimentation on a dSPACE DS1104 of a Direct Voltage Control Scheme”
Journal of Power Electronics
10
(5)
468 
476
DOI : 10.6113/JPE.2010.10.5.468
Abbou A.
,
Nasser T.
,
Mahmoudi H.
,
Akherraz1 M.
,
Essadki A.
2012
“dSPACE IFOC Fuzzy Logic Controller Implementation for Induction Motor Drive”
Journal of Electrical Systems
8
(3)
317 
327
dSpace DS1102 / DS1104 : C programs for induction motor vector control
http://www.baghli.com/dspace.php#Download
Menghal P.M.
,
Laxmi A. Jaya
2011
“Real Time Control of Electrical Machine and Drives: A Review”
International journal of advances in electrical engineering and technology
1
(4)
112 
126
Menghal P.M.
,
Laxmi A. Jaya
Low Cost FPGA Based Replacement for dSPACE Units in the Electric Drives Laboratory
http://cusp. umn.edu/Napa_2013/Friday/Tom_P_Napa.pdf
Singh Bhoopendra
,
Jain Shailendra
,
Dwivedi Sanjeet
“Direct Torque Control Induction Motor Drive with Improved Flux Response”
Hindawi Publishing Corporation Advances in Power Electronics
Article ID 764038
2012
1 
11
ELBACHA A.
,
BOULGHASOUL Z.
,
ELWARRAKI E.
2012
A Comparative Study of Rotor Time Constant Online Identification of an Induction Motor Using High Gain Observer and Fuzzy Compensator”
WSEAS TRANSACTIONS on SYSTEMS and CONTROL
7
(2)
37 
53
Kostić Vojkan
,
Petronijević Milutin
,
Mitrović Nebojša
,
Banković Bojan
2009
“Experimental verification of directtorque control methods for electric drive application”
Automatic Control and Robotics
8
(1)
111 
126
singh Bhoopendra
,
Jain Shailendra
,
Dwivedi Sanjeet
2012
“Experimental Direct Torque Control Induction Motor Drive with Modified Flux Estimation and Speed control Algorithm”
IOSR Journal of Engineering
2
(6)
1296 
1300
DOI : 10.9790/3021026112961300