CONTROL OF AN INDUCTION MOTOR WITH DOUBLE ANN MODEL BASED DTC

David C. Wyld et al. (Eds) : ICAITA, CDKP, CMC, SOFT, SAI - 2016
pp. 63– 71, 2016. © CS & IT-CSCP 2016 DOI : 10.5121/csit.2016.61306
CONTROL OF AN INDUCTION MOTOR
WITH DOUBLE ANN MODEL BASED DTC
Fatih Korkmaz
Department of Electric-Electronic Engineering, Çankırı Karatekin University,
Uluyazı Kampüsü, Çankırı, Turkey
fkorkmaz@karatekin.edu.tr
ABSTRACT
Direct torque control (DTC) is preferably control method on high performance control of
induction motors due to its advantages such as fast dynamic response, simple and robust control
structure. However, high torque and current ripples are mostly faced problems in this control
method. This paper presents artificial neural network (ANN) based approach to the DTC
method to overcome mentioned problems. In the study, by taking a different perspective to ANN
and DTC integration, two different ANN models have been designed, trained and implemented.
The first ANN model has been used for switch selecting process and the second one has been
used for sector determine process. Matlab/Simulink model of the proposed ANN based DTC
method has created in order to compare with the conventional DTC and the proposed DTC
methods. The simulation studies have proved that the induction motor torque and current
ripples have been reduced remarkably with the proposed method and this approach can be a
good alternative to the conventional DTC method for induction motor control.
KEYWORDS
Direct torque control, Induction motor control, Artificial neural networks, Vector control
1. INTRODUCTION
In the early 1970s, vector control method was firstly proposed by Blaschke and it was named as
field oriented control (FOC). About ten years later, another vector based control method was
presented by Takahashi and it was called as direct torque control (DTC). Control of motors with
parameters which converted to veal identities, can be acceptable prominent features of the both
control methods. [1-2].
Due to rapid development in the field of power switches and microcontrollers technologies , the
both methods have shown great development, since they were first presented. When compared
with the FOC, the DTC method has some structural advantages such as simple control algorithm,
robust controller character due to independency of motor parameters, needs only stator phase
resistance, and fast dynamic response. However, the DTC method has some handicaps that can be
listed as, high torque and current ripples, variable switching frequency behavior, and
implementation limitations owing to necessity of low sampling time [3].

64 Computer Science & Information Technology (CS & IT)
Over the last decades, different types of the DTC algorithms have been proposed by researchers
and academics to overcome mentioned handicaps and most of them have focused on torque
ripples side in their studies. In [4], researchers proposed matrix converter based model predictive
control approach and they proved that it can be effective in minimizing torque ripples. In [5],
authors present a modified DTC algorithm for permanent magnet synchronous motor drives with
fast torque dynamics and constant switching frequency. The authors presents the DTC method
using fuzzy controller to minimize torque ripples for BLDC in [6] and for induction motors in [7].
Artificial neural network model based DTC systems have also been investigated to reduce torque
ripples in [8].
This paper presents a different approach to ANN based DTC model to reduce torque ripples on
DTC controlled induction motor drives. Two different ANN models have been designed, trained
and performed in the same model. The first ANN model has been used for switch selecting
process and the second one has been used for sector determine process. Matlab/Simulink model
of the proposed ANN based DTC method was created in order to compare with the conventional
DTC and the proposed DTC methods. Basics of the DTC method was explained in Section 2 and
proposed ANN based DTC method was documented in detail in Section 3. Simulation results
were presented and commented in Section 4.
2. DIRECT TORQUE CONTROL
Mathematical model of the DTC method contains simpler and fewer equations when compared
the FOC. Unlike the FOC, the DTC model needs only one motor parameter in the modelling of
the control system. Thus, the DTC is considered to be more simple and reliable [9].
The both control techniques methodology base on mathematical transformation that transforms
three phase motor parameters to two phase components. The mathematic model of the DTC
system bases on Clarke transformation. Thus, some measured three phase parameters of the
motor are converted to two phase components, such as voltages and currents. Instant values of the
stator flux and produced motor torque can be calculated with these components as defined with
following equations. The Clarke transformation matrix is given as:
൤
f஑
fஒ
൨ =
ଶ
ଷ
቎
1 −
ଵ
ଶ
−
ଵ
ଶ
0
√ଷ
ଶ
−
√ଷ
ଶ
቏ ൥
fୟ
fୠ
fୡ
൩ (1)
Where, fα, fβ are α − β components of motor parameters, and fୟ, fୠ, fୡ are the abc frame
components[10-11].
α − β components of the stator voltages and flux are expressed in following equations.
‫ݒ‬௦ఈ = ܴ௦݅௦ఈ + ‫ܮ‬௦
ௗ௜ೞഀ
ௗ௧
(2)
‫ݒ‬௦ఉ = ܴ௦݅௦ఉ + ‫ܮ‬௦
ௗ௜ೞഁ
ௗ௧
(3)
ߣ௦ఈ = ‫׬‬ሺ‫ݒ‬௦ఈ − ܴ௦݅௦ఈሻ݀‫ݐ‬ (4)

Computer Science & Information Technology (CS & IT) 65
ߣ௦ఉ = ‫׬‬൫‫ݒ‬௦ఉ − ܴ௦݅௦ఉ൯݀‫ݐ‬ (5)
The magnitude of the flux can be calculated with:
ߣ = ටߣ௦ఈ
ଶ + ߣ௦ఉ
ଶ
(6)
and position of the stator flux vector is calculated with:
ߠ = ܽ‫݊ܽݐܿݎ‬
ఒೞഁ
ఒೞഀ
(7)
And finally produced motor torque equation can be written as:
ܶ௘ =
ଷ
ଶ
‫݌‬൫ߣఈ݅ఉ − ߣఉ݅ఈ൯ (8)
In the DTC, motor torque and flux can be controlled directly with control of stator flux vector. In
this control, rotation speed of the flux vector controls produced torque while the length controls
value. The DTC control algorithm is bases on selecting the appropriate inverter switching state to
directly control stator flux vector’s speed and length that explain why the method is named as
DTC. In order to keep the torque and flux errors within the predefined limits, hysteresis
comparators are used [12].
In this control process, six active voltage vectors are used to keep stator flux vector in predefined
hysteresis band limit. The flux vector rotation is divided to six different sectors. Figure 1
represents six sector and active voltage vectors.
Figure 1. Six sector and active voltage vectors
3. ANN MODEL BASED DTC SYSTEM
ANN models of the control systems are inspired by biological nervous systems and
interconnected points, that called neurons, are designed to solve complex problems. In substance,
this structure bases on actually working principles of human brain to solve problems.

In recent decades, ANN models have rising popularity in different kind of control systems due to
their learning abilities, robust structures, beside their modeling success on non-linear and
complex mathematical models.
In the proposed ANN based DTC model, two different ANN models have designed to solve
complex processes in conventional DTC approach; selecting appropriate inverter switching states
and determining stator flux vector region.
α – β components of stator flux were used in the ANN model to determining stator flux vector
region process. The ANN model structure is shown in Figure 2
Figure 2. Structure of ANN model for determining stator flux sector
As shown in Figure 2, flux sector ANN model has two neurons in input layer, six neurons in
hidden layer and one neuron in output layer. The flux sector ANN was trained about 100000
input data which were obtained with conventional DTC model. 70000 input data were used to
train the network while 15000 input data were used for validation and 15000 input data were used
for test processes after training. Simulink model of the flux sector ANN given in Figure 3.
Figure 3. Simulink model of the flux sector ANN unit

Outputs of the flux hysteresis, torque hysteresis and flux region data were used as inputs of the
second ANN model which was created to determine optimum inverter switching states.
Figure 4. Structure of ANN model for determining optimum inverter switching states
As shown in Figure 4, inverter state ANN model has three neurons in input layer, six neurons in
hidden layer and one neuron in output layer. The flux sector ANN was trained about 50000 input
data which were obtained with conventional DTC model. 35000 input data were used to train the
network while 7500 input data were used for validation and 7500 input data were used for test
processes after training. Overall Simulink block diagram of the proposed ANN based DTC
system is shown in Figure 5.
Figure 5. Overall Simulink block diagram of the proposed ANN based DTC system

4. SIMULATIONS
This section presents comparatively simulation results between conventional DTC (C-DTC) and
proposed ANN based DTC (ANN-DTC) models. Two different scenarios were applied and
investigated to obtain fare comparison between both methods. Induction motor and simulation
parameters were presented in Table 1. Total simulation time was 5 sec. and motor load was 0 Nm
at the first 3 sec. then switched to 3 Nm reference torque after 3. sec
Table 1. The induction motor and simulation model parameters
Motor Power (kW) 4
Bus Voltage (V) 300
Phase resistance(ohm) 1,54
Flux Reference (Wb) 0,5
Flux Hysteresis Band ±0,05
Torque Hysteresis Band ±0,5
Sample time (µs) 10
In the first scenario, motor was tested at low speed condition with no-load and loaded conditions.
The motor speed and torque responses are given in Figure 6 and Figure 7, respectively.
Figure 6. Motor speed curves at 250 rpm reference

Figure 7. Motor torque curves at 250 rpm reference
As shown in Figure 6 and Figure 7, motor speed and torque performances are almost same in low
speed working conditions. In both control approach, torque ripples are about ±2 Nm for unloaded
period while torque ripples are about +5/1,7 Nm for loaded conditions. Briefly, it can be said that
there are no meaning difference between the both methods for low speed conditions.
In the second scenario, motor was tested at rated speed (2800rpm) with no-load and loaded
(3Nm) conditions. The motor speed and torque responses are given in Figure 8 and Figure 9,
respectively.
Figure 8. Motor speed curves at 2800 rpm reference

Figure 9. Motor torque curves at 2800 rpm reference
The main difference for the proposed ANN-DTC method emerged in the secondary group of tests
as can be seen clearly in Fig 8 and Fig 9. The speed response of the motor proved that ANN-DTC
method had faster dynamic responses when compared C-DTC method. Time to reach the
reference speed was about %50 shorter in the proposed method. On the other hand, when the
torque curves examined closely, it can be seen that torque ripples of the motor were reduced
remarkably with the ANN-DTC method.
5. CONCLUSIONS
This paper presents a new artificial neural networks based approach to conventional direct torque
control method for high performance control of induction motors. The aim of the paper is
simplifying the complex mathematical process in conventional direct torque control method by
usage of neural network models. For this aim, two independent neural models have been
designed, trained and tested. The first neural model was designed for sectors determine process
and the second one were designed for optimum switching select unit. Numerical simulations have
been performed to investigate the effects of the proposed method on the overall system
performances. Low speed and rated speed performances of the motor were simulated at zero
loads and rated loads working conditions. Simulation results have been comparatively presented
and they have been showed that proposed neural network based DTC method has better dynamic
performance especially closer to rated speed values. The proposed method has faster acceleration
time in transient state region and torque ripples were reduced with the proposed method in
steady-state region. The practical works on the proposed method as a real time application are
still going on and experimental results will be presented by researchers in the future works.
REFERENCES
[1] Blaschke, F.,(1972) “The Principle of Field Orientation Applied to The New Transvector Closed-
Loop Control System for Rotating Field Machines”, Siemens-Rev., Vol. 39, 217–220.

[2] Takahashi Takahashi, I. & Noguchi. T. (1986) “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.
[3] Fatih Korkmaz. & M. Faruk Çakır. & Yılmaz Korkmaz. & Ismail Topaloglu, (2012) “Fuzzy Based
Stator Flux Optimizer Design For Direct Torque Control” International Journal of Instrumentation
and Control Systems (IJICS) Vol.2, No.4, pp 41-49.
[4] S.Kannan , S.Chinnaiya, S.U.Prabha Torque Ripple Minimization of Matrix Converter-Fed PMSM
Drives Using Advanced Direct Torque Control International Journal of Soft Computing, Mathematics
and Control (IJSCMC), Vol. 3, No. 4, November 2014pp. 51-68
[5] G. H. B. Foo and X. Zhang, "Constant Switching Frequency Based Direct Torque Control of Interior
Permanent Magnet Synchronous Motors With Reduced Ripples and Fast Torque Dynamics," in IEEE
Transactions on Power Electronics, vol. 31, no. 9, pp. 6485-6493, Sept. 2016.
[6] D.Premalatha, A.Sakthi Rubini “Direct Torque Control of Brushless DC Motor Using PI and Fuzzy
Controller” International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 4, Issue 4, April 2015 pp.922-926
[7] Korkmaz, Fatih; Topaloğlu, smail; Mamur, Hayati; ,Fuzzy Logic Based Direct Torque Control of
Induction Motor with Space Vector Modulation,"International Journal on Soft Computing, Artificial
Intelligence and Applications",2,5/6,31-40,2015
[8] Bhim Singh, Pradeep Jain, A.P.Mittal, J.R.P.Gupta “Torque Ripples Minimization of DTC IPMSM
Drive for the EV Propulsion System using a Neural Network” Journal of Power Electronics, Vol. 8,
No. 1, January 2008 pp. 23-34
[9] F. Korkmaz “A New Approach to DTC Method For BLDC Motor Adjustable Speed Drives” The
Fourth International Conference on Instrumentation and Control Systems(CICS-2016), vol. 6, no 5,
pp-37–44, April 2016
[10] F. Korkmaz, I. Topaloglu, M.F. Cakir, R. Gurbuz, "Comparative performance evaluation of FOC and
DTC controlled PMSM drives", Power Engineering, Energy and Electrical Drives (POWERENG),
2013 Fourth International Conference on , vol., no., pp.705–708, 13-17 May 2013.
[11] M. Masmoudi, B. E. Badsi and A. Masmoudi, "DTC of B4-Inverter-Fed BLDC Motor Drives With
Reduced Torque Ripple During Sector-to-Sector Commutations," in IEEE Transactions on Power
Electronics, vol. 29, no. 9, pp. 4855-4865, Sept. 2014.
[12] Vas, P., Sensorless Vector and Direct Torque Control, Cambridge University Press, 1998.

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