Biogeography-Based Optimization, BBO, Evolutionary Algorithm, Migration, Partial Migration

Sundarapandian et al. (Eds) : ICAITA, SAI, SEAS, CDKP, CMCA-2013
pp. 161–169, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3813
A NEW FUZZY LOGIC BASED SPACE VECTOR
MODULATION APPROACH ON DIRECT
TORQUE CONTROLLED INDUCTION MOTORS
Fatih Korkmaz, smail Topaloğlu and Hayati Mamur
Department of Electrical and Electronics Engineering
Çankırı Karatekin University, 18200, Çankırı, TURKEY
fkorkmaz@karatekin.edu.tr
ABSTRACT
The induction motors are indispensable motor types for industrial applications due to its well-
known advantages. Therefore, many kind of control scheme are proposed for induction motors
over the past years and direct torque control has gained great importance inside of them due to
fast dynamic torque response behavior and simple control structure. This paper suggests a new
approach on the direct torque controlled induction motors, Fuzzy logic based space vector
modulation, to overcome disadvantages of conventional direct torque control like high torque
ripple. In the proposed approach, optimum switching states are calculated by fuzzy logic
controller and applied by space vector pulse width modulator to voltage source inverter. In
order to test and compare the proposed DTC scheme with conventional DTC scheme
simulations, in Matlab/Simulink, have been carried out in different speed and load conditions.
The simulation results showed that a significant improvement in the dynamic torque and speed
responses when compared to the conventional DTC scheme.
KEYWORDS
Direct torque control, Fuzzy logic control, Space vector modulation, Induction motor control,
1. INTRODUCTION
In electromechanic systems, Direct torque control (DTC) of induction motor is well-known
control scheme which provides fast dynamic response compared with other control schemes like
field oriented control (FOC). DTC has been proposed for induction motor control in 1985 by
Takahashi [1] and similar idea that the name of Direct Self Control devoloped in 1988 by
Depenbrock [2].
Over the past years, DTC has gained great attention due to its advantages like simple structure,
robustness to parameters variations, fast dynamic response, not need to current regulators...etc.
However, like every control scheme, DTC has some disadvantages too. If we want to sort out
some of these disadvantages; difficulty to control torque and flux at very low speed, high current
and torque ripples, variable switching frequency behavior and high sampling frequency needed
for digital implementation.
In order the overcome these disadvantages many researchers have been researching on the DTC
drive and they can grouped under several headings:

Computer Science & Information Technology (CS & IT) 162
• Using different switching techniques and inverter topologies [3-7]
• Using artificial intelligence on different sections of system [8-9]
• Using different observer models [10-11]
In this paper, a new fuzzy logic based space vector modulation method has been proposed to
improve torque behavior of induction motor on the DTC scheme. The fuzzy logic controller in the
proposed method rates of flux and torque errors and describes optimum space vector to minimize
flux and torque errors. The experimantal studies have performed with dSPACE 1103 controller
board to performance testing of the proposed control method. . In Section II of this paper, the
basic principles of DTC has shortly introduced and in Section III, detailed information about the
proposed method has presented. Section IV presents the experimental results of the the proposed
method. Finally, conclusion has given in Section V.
2. DIRECT TORQUE CONTROL
In conventional DTC scheme, the control of an induction motor involves the direct control of
stator flux vector by applying optimum voltage switching vectors of the inverter . For this control,
the stator current should be decoupled two independent components as flux and torque
components like dc motors. The clarke transformation method is uses in this decoupling process
in the DTC scheme.
The DTC bases on the selection of the optimum voltage vector which makes the flux vector rotate
and produce the demanded torque. In this rotation, the amplitude of the stator flux vector remains
in hysteresis band limits[12]. Stator flux linkage vector is estimated using (8)-(10).
,)( dtiRV s∫ −= αααλ (1)
,)( dtiRV s∫ −= βββλ (2)
.22
βα λλλ += (3)
Where λ is stator flux vector, αv and βv stator voltages two phase components, αi and βi line
currents in α-β reference frame and sR describes stator resistance. The electromagnetic torque of
the induction machine can be calculated as given in Eq. 7
).(
2
3
αββα λλ iipTe −= (4)
Where, p is the number of pole pairs. In DTC scheme, stator flux rotate trajectory devided six
region and well- defined of stator flux region is directly affects on control performance and
calculation of stator flux vector region as given in Eq. 8.
).(tan 1
α
β
λ
λ
λ
θ −
= (5)
These estimated values are compared to reference values and the resultant errors are applied to
the hysteresis comparators. Two different hysteresis comparators, as flux and torque comparators,
generate other control parameters on the DTC scheme. Flux hysteresis comparator is two level
type while torque comparator is tree level type. According to the hysteresis comparators outputs,

163 Computer Science & Information Technology (CS & IT)
the estimated angle of flux linkage and using a switching table, optimum voltage vectors are
selected and applied to the inverter. The basic schematic representation of DTC scheme for an
induction motor is shown in Figure 1.
Figure 1. Conventional DTC scheme
3. THE FUZZY BASED SVM-DTC
The objective of space vector pulse width modulation technique is to obtain the demanded output
voltage, Uout, by instantaneously combination of the switching states corresponding the basic
space vectors (Figure 2.)[13].
Figure 2. Basic space vectors
outU can be obtained as Eq. 6. by applying the inverter in switching states xU and 60+xU or
60−xU for the time periods, 1T and 2T periods of time respectively.
)(
1
)( 6021 ±+= xxout UTUT
T
nTU (6)

It must be pointed out that the sum of 1T and 2T periods should be less than or equal to total
sampling time period, T. If TTT 〈+ 21 , than the inverter needs to be in pasive vectors,
0000 or 1110 states, for the rest of the total time period that pasive time period can be named 0T .
Thus, the calculation of total time period is given in Eq. 7.
TTTT =++ 021 (7)
Therefore, Eq. 6 becomes Eq. 7 in the following,
)00( 11100006021 orTUTUTTU xxout ++= ± (8)
From Eq. 8., we get Eq. 9. for 1T and 2T .
[ ] [ ] outxx UUUTTT 1
6021
−
±=τ
(9)
where [ ] 1
60
−
±xx UU is the normalized decomposition matrix for the sector.
Assume the angle between outU and xU is α from Figure 3, it can be also obtained Eq. 10. and
Eq. 11. for the 1T and 2T [13].
)30cos(21 °+= αoutUTT (10)
)sin(22 αoutUTT = (11)
Figure 3. The Simulink block diagram the proposed scheme
The proposed fuzzy based SVM-DTC scheme includes a fuzzy logic controller to produce
optimum control vector. The optimum control vector angle is calculated by fuzzy logic controller
with instantaneously flux and torque errors. On this calculation, fuzzy logic rates both errors and
produces necessary change in vector angle for next step. Then, calculated optimum vector angle
applied to discrete space vector pulse width modulation block (DSV-PWM) and DSV-PWM
generates switching signals. The Simulink block diagram of the proposed system is given in
Figure 3.

The membership functions of fuzzy logic controller flux-torque inputs and angle output can be
seen in Figure 4. Table 1. describes rule table of fuzzy logic controller.
Figure 4. Flux, torque and angle membership functions
Table 1. Rule table of fuzzy logic controller
Rules
Flux
BD SD SI BI
Torque
BD -135 -105 -75 -45
SD -165 -135 -45 -15
0 0 0 0 0
SI 165 135 45 15
BI 135 105 75 45

4. SIMULATIONS
In order to evaluate the effectiveness of the proposed fuzzy logic based SVM-DTC method
simulation works have been carried out in Matlab/Simulink software. The DTC scheme and the
induction machine used in the simulation works have the parameters given in Table 2.
Table 2. Induction Machine and Simulation parameters
IM and Simulation Parameters
Inverter bus voltage (V) 400V
Rated Power (kW) 4
Stator resistance ( ) 1.405
Stator inductance(H) 0.0058
Pole pairs 2
Sampling time (µs) 50
Flux reference (Wb) 0.8
Some tests have been carried out to compare the performances of the proposed fuzzy logic based
dSV-PWM DTC (FL-dSV-PWM) with conventional DTC (C-DTC). In order to compare the
performances with the C-DTC and the proposed FLSVM-DTC on induction motor drive different
speed and load range applied to the induction motor. The dynamic performances of the schemes
are performed by applying step change on load, 0Nm to 10 Nm, at 0,5. sec.
In first step of the simulation studies, the induction motor has been tested at rated speed with two
different load conditions. The simulation results of speed and torque responses at 1500 rpm
reference are shown in Figure 5. and Figure 6., respectively.
(a) (b)
Figure 5. Speed curves of motor at 1500 rpm
a) C-DTC b) FLSVM-DTC

(a) (b)
Figure 6. Torque curves of motor at 1500 rpm
According to the speed and torque curves which given in Figure 5. and Figure 6., the motor has
reached the reference speed at 0,075. sec. for both control scheme. So, it can be said that there are
no difference between C-DTC and FLSVM-DTC and the motor has almost same performance at
transient conditions for both control scheme. However, the main differences have appeared at
steady state conditions. It can be seen that, with the FLSVM-DTC instantaneous speed
fluctuations and torque ripples of the motor are reduced significantly.
In the second step of simulation studies, the motor has been at low speed with two different load
conditions. The simulation results of speed and torque responses at 250 rpm reference are shown
in Figure 7. and Figure 8., respectively.
(a) (b)
Figure 7. Speed curves of motor at 250 rpm

(a) (b)
Figure 8. Torque curves of motor at 250 rpm
According to the speed and torque curves which given in Figure 7. and Figure 8., the motor has
reached the reference speed at 0,02. sec for both control scheme and still no difference at transient
conditions. If we need to compare of steady state conditions of the motor for both control scheme,
it must be pointed out that the FLSVM-DTC controlled motor has better performance as lesser
torque ripples and speed fluctuations.
5. CONCLUSIONS
In this paper, a new fuzzy logic based space vector modulation technique has been proposed for
direct torque controlled induction motor drives. The numerical simulations have been carried out
to verify the proposed technique under different load and speed conditions. The numerical
simulations proves that torque and speed responses of the motor are significantly improved with
the proposed technique. The torque ripples of the motor are lesser about % 40, in parallel, the
speed fluctuations are reduced according to conventional direct torque control technique.
Moreover, the hyseteresis controllers and look-up table that used in conventional scheme are
removed. Thus, switching frequency is maintained constant and the complexity of the scheme has
been reduced.
REFERENCES
[1] I. Takahashi and T. Noguchi , “A new quick-response and high efficiency control strategy of an
induction motor” IEEE Transactions on Industrial Applications, vol.I A-22 , no.5, pp. 820–827, 1986.
[2] M. Depenbrock, “Direct self control of inverter-fed induction machines” IEEE Transactions in Power
Electronics, vol. PE-3, vo. 4, pp. 420–429, 1988.
[3] B. Xu and X. Zhang , “Design of a new Direct Torque Control in induction motor” Control and
Decision Conference, (CCDC '09),– P.5397–5400. 2009.
[4] D. Casadei, G. Serra and A. Tani, “The use of matrix converters in direct torque control of induction
machines” IEEE Trans. on Industrial Electronics, vol.48, no.6, pp. 1057–1064, 2001.
[5] Batna University, “Improvement in DTC-SVM of AC Drives Using a New Robust Adaptive Control
Algorithm” International Journal of Control, Automation, and Systems, vol. 9, no. 2, pp.267-275,
2011.
[6] D. Casadei, G. Serra and A. Tani, “Implentation of a direct torque control algorithm for induction
motors based on discrete space vector modulation” IEEE Trans. on Power Electronics, vol.15, no. 4,
pp. 769–777, 2000.

[7] K.-B. Lee and F. Blaabjerg, “Improved Direct Torque Control for Sensorless Matrix Converter Drives
with Constant Switching Frequency and Torque Ripple Reduction” International Journal of Control,
Automation, and Systems, vol. 4, no. 1, pp.113-123, 2006.
[8] S. Benaicha, F. Zidani, R.-N. Said, M.-S.-N. Said, “ Direct Torque with Fuzzy Logic Torque Ripple
Reduction Based Stator Flux Vector Control“ Computer and Electrical Engineering, (ICCEE '09),
vol.2, pp. 128–133, 2009.
[9] N. Sadati, S. Kaboli, H. Adeli, E. Hajipour and M. Ferdowsi, “Online Optimal Neuro-Fuzzy Flux
Controller for DTC Based Induction Motor Drives” Applied Power Electronics Conference and
Exposition (APEC 2009),– P.210–215. 2009.
[10] Z. Tan, Y. Li and Y. Zeng, “A three-level speed sensor-less DTC drive of induction motor based on a
full-order flux observer” Power System Technology, Proceedings. PowerCon International
Conference, vol. 2, pp. 1054- 1058, 2002.
[11] G. Ya and L. Weiguo, “A new method research of fuzzy DTC based on full-order state observer for
stator flux linkage” Computer Science and Automation Engineering (CSAE), 2011 IEEE International
Conference, vol. 2, pp.104-108, 2011.
[12] P. Vas, “Sensorless vector and direct torque control” – Oxford University Press, 2003.
[13] Zhenyu Yu "Space-Vector PWM With TMS320C24x/F24x Using Hardware and Software
Determined Switching Patterns" Texas Ins. Application Report, SPRA524, pp. 5., 1999.
Authors
Fatih Korkmaz was born in Kırıkkale, Turkey, in 1977. He received the B.T., M.S., and
Doctorate degrees in in electrical education, from University of Gazi, Turkey, respectively
in 2000, 2004 and 2011. His current research field includes Electric Machines Drives and
Control Systems
smail TOPALOGLU was born in Adana, Turkey, in 1983. He received the B.Sc , M.Sc.
and Ph.D degrees in electrical education from University of Gazi in 2007,2009 and 2013,
respectively. His current research interests include Computer aided design and analysis of
conventional and novel electrical and magnetic circuits of electrical machines, sensors
and transducers, mechatronic systems
Hayati Mamur was born in Bolu, Turkey, in 1974. He received the B.Sc , M.Sc. and
Ph.D degrees in electrical education from University of Gazi in 1996,2005 and 2013,
respectively. His research interests include automatic control, SCADA, PLC,
microcontroller, DSP control applications, renewable energy, and thermoelectric
modules.

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