A Novel Direct Torque Control for Induction Machine Drive System with Low Torque and Flux Ripples using XSG

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 4, No. 4, December 2014, pp. 517~527
ISSN: 2088-8694  517
Journal homepage: http://iaesjournal.com/online/index.php/IJPEDS
A Novel Direct Torque Control for Induction Machine Drive
System with Low Torque and Flux Ripples using XSG
Souha Boukadida, Soufien Gdaim, Abdellatif Mtibaa
Laboratory EµE of the FSM, University of Monastir, Tunisia
Article Info ABSTRACT
Article history:
Received May 6, 2014
Revised Sep 27, 2014
Accepted Oct 11, 2014
The conventional Direct Torque Control (DTC) is known to produce a quick
and robust response in AC drives. However, during steady state, stator flux
and electromagnetic torque which results in incorrect speed estimations and
acoustical noise. A modified Direct Torque Control (DTC) by using Space
Vector Modulation (DTC-SVM) for induction machine is proposed in this
paper. Using this control strategy, the ripples introduced in torque and flux
are reduced. This paper presents a novel approach to design and
implementation of a high perfromane torque control (DTC-SVM) of
induction machine using Field Programmable gate array (FPGA). The
performance of the proposed control scheme is evaluated through digital
simulation using MatlabSimulink and Xilinx System Generator. The
simulation results are used to verify the effectiveness of the proposed control
strategy.
Keyword:
DTC-SVM
FPGA
Induction machine
Matlab/Simulink
XSG
Copyright © 2014 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Souha Boukadida,
Laboratory EµE of the FSM
University of Monastir, Tunisia
Email: boukadidasouha@yahoo.fr
1. INTRODUCTION
Since its inception, the Direct Torque Control has gained popularity for induction machine drives.
Indeed, the control variables that are the stator flux and torque are calculated from the quantities related to
the stator without the intervention of mechanical sensor. The response of the DTC is fast, however it has
some drawbacks such as notable torque and flux ripples and the variable commutation frequency behavior of
the inverter. Many papers presented different approaches to minimize the flux and torque ripples [1]-[4]. In
[1] and [3], electromagnetic torque and flux are controlled directly by the selection of a switching vector
from a table selection. Nevertheless, the selected vector is not always the best one because only the sector is
considered, where the flux space vector lies without considering its location.
To overcome the several disadvantages of DTC a new control technique called Direct Torque
Control – Space Vector Modulated (DTC-SVM) [5]-[6] is developped. In this new method, the disadvantages
of the DTC are eliminated. The DTC-SVM strategies are based on the same fundamentals as classical DTC;
it provides dynamic behavior comparable with classical DTC.
In practice, the vector control algorithm for an induction machine is implemented utilizing digital
signal processor (DSP). The DSP control procedure is performed sequentially; this may result in a slower
cycling period if complex algorithms are involved. Employing field programmable gate array (FPGA) in
implementing vector control strategies provides advantages such as simpler hardware and software design,
rapid prototyping, hence fast switching frequency and high speed computation [7]-[8].
The paper devotes to a comparative study between the performances of two approaches: (i) Classical
DTC (ii) DTC-SVM. These strategies are designed using Xilinx System Generator (XSG) and
Matlab/Simulink software packages and implemented on FPGA controller.

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IJPEDS Vol. 4, No. 4, December 2014 : 517 – 527
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2. BASIC PRINCIPLE OF DTC
The main idea of DTC is to recover the reduction of the ripples of torque and flux, and to have
superior dynamic performances. Figure 1 present a possible schematic of Direct Torque Control. There are
two different loops corresponding to the magnitudes of the stator flux and torque. The error between the
estimated stator flux magnitude φs and the reference stator flux magnitude φs*is the input of a two level
hysteresis comparator whereas the error between the estimated torque Te and the reference torque Te* is the
input of a three level hysteresis comparator. The outputs of the stator flux error and torque error hysteresis
blocks, together with the position of the stator flux are used as inputs of the switching table.
Figure 1. Block diagram of DTC
The selection vector is based on the hysteresis control of the torque and the stator flux. In the basic
form the stator flux φs is estimated with:
0
( )
t
s s sV R i dt   (1)
The stator voltage and stator current are calculated from the state of three phase (Sa ,Sb ,Sc) and
measured currents (ia, ib, ic).
2 4
3 3
0
2 4
3 3
2
( , , ) ( )
3
2
( , , ) ( )
3
j j
s a b c a b c
j j
s a b c a b c
V S S S E S S e S e
i i i i i i e i e
 
 
  
  
(2)
Phase angle and stator flux amplitude are calculated in expression (3).
2 2
( )s
s
s
s s s
arctg 

 



  

 
(3)
The developed electromagnetic torque Te of the machine can be evaluated by Equation (4):
3
( )
2
e s s s sT p i i      (4)
The stator flux vector is moving along a straight axis colinear to that of the voltage vector required
by the inverter:

IJPEDS ISSN: 2088-8694 
A Novel Direct Torque Control for Induction Machine Drive System with Low Torque… (Souha Boukadida)
519
Figure 2. Stator flux vector evolution in the first sector
3. DTC SPACE VECTOR MODULATION
The DTC algorithm is based on the instantaneous values and directly calculated the gate signals for
the inverter. The control algorithm in DTC-SVM is based on average values whereas the switching signals
(Sa, Sb and Sc) for the inverter are calculated by space vector modulator [9]-[11].
3.1. Principle of Vector MLI
For each period of modulation of the inverter, the three phase voltages provided by the control
algorithm can be expressed in a fixed reference linked to the stator, through their projections Vsα and Vsβ.
The inverter has six switching cells, giving eight possible switching configurations. These eight
switching configurations can be expressed in the plane (α, β) by 8 vectors tensions.
Knowing that in the graduation phase voltages (Va, Vb, Vc) are represented in the plane by a vector
Vs. The principle of vector MLI is to project the desired stator voltage vector Vs on the two adjacent vectors
corresponding to two switching states of the inverter. The values of these projections provide the desired
commutation times.
3.2. General Structure of the Control DTC-SVM
Most existing blocks in the control DTC-SVM are identical to those of control DTC as shown in the
following figure (3). The new blocks will be discussed below.
Figure 3. Block diagram of DTC-SVM
3.3. Calculation of time of application of the status of the inverter
Each modulation period Tmod of the inverter, the projected vector Vs on the two adjacent vectors
assures the switching time of calculation.
The key step of the SVM technique is the determination of Ti and Ti+1 during every modulation
period Tmod. To illustrate the methodology we consider the case where Vs can be compounded by the active
voltage vectors V1 and V2. The projection of the reference voltage vector on V1 and V2 is illustrated in the
following figure:

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Figure 4. Projection of the reference voltage vector on V1 and V2
The active voltage vectors V1 and V2 are given as follow:
0
1
3
2
2
. .
3
2
. .
3
j
j
V E e
V E e





(5)
Expressing the voltage vector Vs in the graduation (α, β) we have:
1 2
1 2
m od m od
s s s
T T
V V jV V V
T T
    
  
(6)
Expanding this equation it is possible to express the time T1 and T2 in terms of Vsα and Vsβ. The
conduction time will be expressed as follows:
mod
1
mod
2
3 1
( . ).
2 2
2. .
s s
s
T
T V V
E
T
T V
E
 

 

(7)
To facilitate the calculations, we normalize the voltages Vsα and Vsβ by posing:
^
s
s
^
s
s
V
V 2
E
V
V 2
E






(8)
Consequently, the duties expressions are given as follows:
^ ^
s s1
^
s2
0 1 2
3 1
.V .V
2 2
V
1
D
D
D D D
 

 

   (9)
The space vector in sector 1 is shown in figure (5).The time duration of zero vectors is divided
equally into (V0, V1, V2, V7, V2, V1, V0), whereas the time duration of each nonzero vector is distributed
into two parts. This sequence can ensure that is one phase switches when the switching pattern switches, thus
can reduce the harmonic component of the output current and the loss of switching devices.

521
Figure 5. Sequences of the switches states in sector N1
The duties of each phase of the inverter are presented as follows:
1 2
1 2
1 2
0.5(1 )
0.5(1 )
0.5(1 )
a
b
c
S D D
S D D
S D D
  
  
   (10)
4. SIMULATION AND RESULT
The DTC and DTC-SVM scheme for induction machine are simulated using Matlab/Simulink and
Xilinx System Generator and their results have been compared. The machine parameters used for simulation
are given in this table.
Table 1. Induction Machine parameters
Voltage 220/380 v
Stator resistance Rs 5.717 Ω
Rotor resistance Rr 4.282 Ω
Stator inductance Ls 0.464 H
Rotor inductance Lr 0.464 H
Mutual inductance M 0.441 H
Moment of inertia J 0.0049 Kg.m2
4.1. Simulink Model of Direct Torque Control
The simulation of DTC was conducted using SimulinkMATLAB. The inverter switching pulses are
obtained from the switching table which decides the pulses from the error signals of torque and flux. The
overall DTC model is shown in Figure 6.
Figure 6. Simulink Model of DTC

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4.2. Simulink Model of Space Vector Modulated Direct Torque Control obtained with
MatlabSimulink
Figure 7 illustrate the simulation block of the DTC-SVM control. The system is composed of the
machine, PI controllers, three phase voltage source inverter, reference frame transformation blocks
Concordia and Park. The Insulated-gate bipolar transistor IGBT switches are controlled using space vector
modulation technique.
Figure 7. Simulink Model of DTC-SVM
The simulation of this technique is made through the following model:
Figure 8. Simulink Model of bloc SVM
4.3. Simulink Model of Space Vector Modulated Direct Torque Control obtained with Xilinx System
Generator
Initially, an algorithm is designed and simulated at the system level with the floating-point Simulink
blocksets. A hardware representation of FPGA implementation is then derived using XSG. The XSG
provides a bit-accurate model of FPGA circuits and automatically generates a synthesizable VHDL code for
implementation in Xilinx FPGA. For DTC-SVM modeling, the blocks used are mostly multipliers, adders,
Cordic sin cos, etc. The detailed steps are shown in the following diagram in Figure 9. The XSG design of
proposed DTC-SVM is shown in Figure 10. The block Calcul_Vsalpha_Vsbeta is used to project the three-
phase voltages in the repository (α, β) by performing the processing Clarke as shown in Figure 10(a). The
block SVM generates a series of pulses to be used subsequently to carry out the control signals used in the
model of the inverter as shown in Figure 10(b) and 10(c). The XSG design of torque and flux estimator is
shown in Figure 11-12.

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Figure 9. Induction machine drive controller design and implementation process
Figure 10. Xilinx Model of SVM
Figure 10(a). Calcul Vsalpha Vsbeta Figure 10(b). VM bloc in XSG

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Figure 10(c). Calcul Sc
Figure 11. Model of electromagnetic torque Figure 12. Model of flux estimator
4.4. SIMULATION RESULTS
The performance of the induction machine under different operating conditions was also
investigated in order to verify the robustness of the proposed control scheme. The steady state behavior of
induction machine with the conventional DTC and DTC-SVM are illustrated in Figure 13-15.
It is possible to see in Figure 13(a), (b), (c) an appreciable reduction of electromagnetic torque
ripple has been obtained using the DTC-SVM. For the DTC, torque variation of the hysteresis band equal to
1.1. The high ripple observed in the DTC is reduced when we use the DTC-SVM, because in SVM, many
vectors (IGBT states) are selected to adjust the flux and torque ripple in each sample time, whereas in DTC
just one vector is selected to adjust ripple inside hysteresis bands of flux. Using SVM control provides the
system with minimum ripple for flux as shown in Figure 14, where the flux ripple percentage is about 0.92%.
The DTC-SVM of induction machine presents the advanced performance to achieve tracking of the
desired smooth circular trajectory of stator flux locus shown in Figure 15.
Figure 13. Electromagnetic torque, (a): DTC using MATLAB, (b) DTC-SVM using MATLAB, (c): DTC-
SVM using XSG

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Figure 134. Stator flux DTC using MATLAB, (b): DTC-SVM using MATLAB,
(c): DTC-SVM using XSG
Figure 15. Trajectory of stator flux: DTC using MATLAB, (b): DTC-SVM using MATLAB, (c): DTC-SVM
using XSG
Table 2. The percentage flux and torque error for DTC and DTC-SVM
Control strategies Flux ripple (%) Torque ripple (%)
DTC using Matlab 5.52 11
DTC-SVM using Matlab 0.92 1
DTC-SVM using XSG 1.84 2
The best results are given by DTC-SVM using MATLABSIMULINK, this is due to the arbitrary
choice of the number of bits at XSG.
5. FPGA SIMULATION RESULTS OF DTC-SVM
The above designed model is implemented using FPGA Editor. FPGA Editor reads the NCD file
generated by the Map or Place & Route process, which contains the logic and routing of the design mapped
to components, such as CLBs and IOBs.The internal structure of FPGA is shown in Figure 16.
Figure 146. Internal structure of FPGA

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The result of the resources used is shown in the following table:
Table 3. The result of the resources
Slice logic utilization Used Available Utilization
Number of slices LUTs 10,511 44,800 23%
Number used as Logic 9,869 44,800 22%
Number of DSP48Es 109 128 85%
Number of slice registers 655 44,800 1%
6. CONCLUSION
This paper has been devoted to the comparison between the performances of the DTC and DTC-
SVM strategy. The steady state features of the induction machine as well as the transient behavior under both
approaches have been commented and compared. The simulation result clearly indicates the high
performance of DTC-SVM. The proposed high performance scheme is designed using XSG and
Matlab/Simulink blocksets and implemented on Xilinx Virtex 5 FPGA. Numerical simulations have been
carried out showing the advantages of the DTC-SVM with respect to the DTC. This work is the first step
towards implemetation on FPGA of DTC-SVM. Future work will extend this experimental validation to the
study.
REFERENCES
[1] Z Li, L Wang, S Zhang, C Zhang, J Ahn. Torque Ripple Reduction in Direct Torque Controlled Brushless DC
Motor. IEEE Trans. Electrical Machines. 2011; 1-4.
[2] Y Cho, D Kim, K Lee, Y Lee, J Song. Torque Ripple Reduction and Fast Torque Response Strategy of Direct
Torque Control for Permanent- Magnet Synchronous Motor. IEEE Trans.Ind.Electronics. 2013; 1-6.
[3] J Beerten, J Verveckken, J Driesen. Predictive Direct Torque Control for Flux and Torque Ripple Reduction. IEEE
Trans.Ind.Electronics. 2010; 57(1).
[4] T Sutikno, N Rumzi, N Idris, A Jidin, N Cirstea. An Improved FPGA Implementation of Direct Torque Control for
Induction Machines. IEEE Trans.Ind.Electronics. 2013; 1280-1290.
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Machine Based on Indirect Matrix Converter. International Journal of Research and Reviews in Computing
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[7] MW Naouar, E Monmasson, AA Naassani. FPGA-based current controllers for AC machine drives-A review.
IEEE Transactions on Industrial Electronics. 2007; 54(4): 1907- 1925.
[8] JJ Rodriguez-Andina, MJ Moure, MD Valdes. Features, design tools, and application domains of FPGAs. IEEE
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[9] Habetler TG, Profumo F, Pastorelli M. Direct torque control of induction machines over a wide speed range.
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BIOGRAPHIES OF AUTHORS
Souha BOUKADIDA received the degree in Electrical Engineering from National School of
Engineering of Monastir, Tunisia in 2012. In 2013 she received his M.S degree in Automatic and
Diagnostic from Moanstir University. Her current research interests include rapid prototyping
and reconfigurable architecture for real-time control applications of electrical system.

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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 real-
time 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 real-time control applications of electrical system.
Abdellatif MTIBAA is currently Professor in Micro-Electronics and Hardware Design with
Electrical Department at the National School of Engineering of Monastir and Head of Circuits
Systems Reconfigurable ENIM-Group at Electronic and microelectronic Laboratory. He holds a
Diploma in Electrical Engineering in 1985 and received his PhD degree in Electrical
Engineering in 2000. His current research interests include System on Programmable Chip, high
level synthesis, rapid prototyping and reconfigurable architecture for real-time 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 co-organizer of several international conferences.

A Novel Direct Torque Control for Induction Machine Drive System with Low Torque and Flux Ripples using XSG

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