Adaptive neural network basedcontroller for direct torque

control of PMSM with minimumtorque ripples and EMI

noise reductionKayhan Gulez

Electrical Engineering Department, Yildiz Technical University, Istanbul, Turkey

Abstract

Purpose – The paper aims to provide an adaptive neural network controller for permanent magnetsynchronous motor (PMSM) under direct torque control (DTC) algorithm to minimize the torque rippleand EMI noise.

Design/methodology/approach – The design methodology is based on vector control used forelectrical machines. MATLAB simulations supported with experimental study under Cþþ are used.

Findings – The simulated and experimental results show that considerable torque ripple as well ascurrent ripple and EMI noise reduction can be achieved by utilizing adaptive neural switchingalgorithm to fire the inverter supplying the PMSM.

Research limitations/implications – This research is limited to PMSM, however the research canbe extended to include other AC motors as well. In addition, the following points can be studied: theeffects of harmonics in control signals on the torque ripple can be analyzed; the actual mathematicalrelation between the torque and flux ripple can be studied to set the flux and torque bands width inreasonable value; different neural network algorithms can be applied to the system to solve the similarproblems.

Practical implications – Based on existing DTC control system, it is only required to change thesoftware switching algorithm, to provide smooth torque, given that the switching frequency of theinverter module is more than or equal to 15 MHz and the system is supplied with timers. In additiona relatively higher DC voltage may be required to achieve higher speed compared with thetraditional DTC.

Originality/value – In this paper, the stator flux position, and errors due to deviations fromreference values of the torque and stator flux are used to select two active vectors while at the sametime the absolute value of the torque error and the stator flux position are used neural networkstructure to adapt the switching of the inverter in order to control the applied average voltage level insuch a way as to minimize the torque ripple, so instead of fixed time table structure, a neural networkcontroller is used to calculate the switching time for the selected vectors and no PI controller is used asthe case in the traditional space vector modulation. This work is directed to motor drive systemdesigners who seek highly smooth torque performance with EMI noise reduction.

Keywords Torque, Electric motors, Neural nets, Electromagnetism

Paper type Research paper

1. IntroductionOwing to high-power density and high-torque/inertia ratio, permanent magnetsynchronous motor (PMSM) are widely used in high-performance drives such aselectrical vehicles, robotic, and servo applications. The controlling of the torque of

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COMPEL: The International Journalfor Computation and Mathematics inElectrical and Electronic Engineering

Vol. 27 No. 6, 2008pp. 1387-1401

q Emerald Group Publishing Limited0332-1649

DOI 10.1108/03321640810905855

PMSM usually follows either field-oriented control (FOC) or the most popularhysteresis direct torque control (HDTC).

The basic principles of HDTC of PMSM (Buja and Kazmierkowski, 2004; Zhong et al.,1997; Chung et al., 1998; Vas, 1998; Dan et al., 1998; Luukko, 2000) involves direct controlof stator flux linkages and generated electromagnetic torque by applying optimumvoltage switching vectors to the inverter supplying the motor. Although, HDTCcompared with rotor FOC has many advantages such as fast torque response,elimination of the d-q axis current controllers and elimination of rotor position requiredfor transformation, it has many disadvantages such as switching and current harmonicssupplied by the power inverter that updated once only when the outputs of thehysteresis controllers change states. Thus, the switching of the power inverter in HDTCconstitutes the major source of harmonics in PMSM and leads to variable switchingfrequency and associated with large harmonic range and high-current ripples.

These harmonics cause many unwanted phenomena such as electromagneticinterference (EMI) and parasitic torque pulsation and the associated mechanicalvibration and acoustic noise.

Recently, many research efforts have been carried out (Tan et al., 2001; Martins et al.,2002; Swierczynski et al., 2002; Tang et al., 2004) to reduce some of these drawbacks withdifferent degree of success. However, the conventional controllers are very sensitive toparameter variations, and load disturbance. Therefore, intelligent controllers such asfuzzy and neural network systems are normally needed to deal with such situations.

Artificial neural network (ANN) has been applied to a wide range of dynamicsystem applications in recent years. Advantages of ANN controllers over theconventional ones presented in robustness, parallel distributed structure, and ability tolearn as well as capability of handle nonlinear situations. These advantages supportthe ANN in playing a major role in solving uncertainty problems in motor drivesystems, specially when HDTC is used, where then, the stator resistance variation anddc voltage measuring operations in existence of high-switching surges, leads touncertainty in torque and flux estimation which in turn leads to torque ripple and inworst case it may lead to generation of breaking torque. In the field of ANN controllers,many research efforts have been achieved to deal with PMSM control algorithms(Xiaodong et al., 2004; Li et al., 1998; Liu et al., 1998; Zhang et al., 2006; Cao et al., 2006;Mobarakeh et al., 2000; Yi et al., 2003; Gulez et al., 2007).

In this study, an application of adaptive ANN-based controller for PMSM underhigh-performance direct torque control (DTC) to minimize the torque ripples ispresented. In this system, the stator flux position, and errors due to deviations fromreference values of the torque and stator flux are used to select two active vectors whileat the same time the absolute value of the torque error, flux error and the stator fluxposition are used in ANN block to adapt the switching of the inverter in order to controlthe applied average voltage level in such a way to minimize the torque ripples.

2. Structure of the control systemFigure 1 shows the basic structure of the proposed ANN-based DTC control system ofPMSM. The inputs to the system are the reference flux (Cf) and the reference torque T*ewhen torque control is required to be achieved or the reference speed (vf) when speedcontrol is intended. In the speed control loop a conventional PI controller is used toobtain the required torque reference.

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2.1 Modeling of PMSMThe motor equations in rotor reference frame are given as:

vsd

vsq

" #¼

R þ pLsd 2PvrLsq

PvrLsd R þ pLsq

" #isd

isq

" #þ

0

PvrcF

" #; ð1Þ

Te ¼3

2PðcFisq þ ðLsd 2 LsqÞisdisqÞÞ; ð2Þ

dvm

dt¼

ðTe 2 TL 2 bvmÞ

J; ð3Þ

durdt

¼ vr; ð4Þ

where, vsd, vsq – d- and q-axis stator voltages; isd, isq – d- and q-axis stator currents;R – stator winding leakage resistance; Lsd, Lsq – d- and q-axis stator inductances;p ¼ d/dt – differential operator; P – number of pole pairs of the motor; vr ¼ Pvm – isthe rotor electrical speed; cF: rotor permanent magnetic flux; Te – generatedelectromagnetic torque; ur – is the rotor electrical position.

HDTC for PMSM is normally achieved with two hysteresis controllers, one forstator flux magnitude error and the other for the torque magnitude error. Theselection of one switching vector per sampling time depends on the sign of these twocontrollers without inspections of the magnitude of the errors produced in thetransient and dynamic situations per sampling time also the level of the applied statorvoltage is not inspected. It has been shown recently that the dynamic of the torquecan be controlled by controlling the level of the torque error and flux error magnitude(Li et al., 1998; Liu et al., 1998; Zhang et al., 2006; Cao et al., 2006; Mobarakeh et al.,2000; Yi et al., 2003; Gulez et al., 2007), and these two criteria are functions of theapplied stator voltage, so controlling the average level of applied voltage according to

Figure 1.Basic structure of the

control system

ActiveVk1 & Vk2Selector

IGBTSYSTEM

NeuralNetwork

Estimated Flux andTorque

PMSM∆Te

∆ψe

λs

Vk

ψs Sensed

Currents

SensedVDC

Te*

Te

λe

_

_

t1, t2PI

ωf

_

Sensed speed

ψf

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these errors can result in minimum torque ripples. Therefore, this criterion can beexpressed as:

Te ¼ f ðV s;DCðV sÞ;DTeðV sÞÞ: ð5Þ

Equation (5) suggests nonlinear relationship between the dynamic torque and theaverage applied stator voltage per every sampling time. Therefore, using fuzzy logicor ANN controller, in this situation, may result in controlled torque with minimumripples.

3. The proposed control circuitThe component of the proposed control circuit shown in Figure 1 can be explained thefollowing subsections.

3.1 Active vector selectorThe active vector selector block contains algorithm to select two active vector Vk1 andVk2, in response to the output of the hysteresis controllers of the flux error (f) and thetorque error (t); as well as the stator flux position sector (n) (Gulez et al., 2007). Thevector selection set is shown in equation (6). It should be noted that no zero vector(V0 or V7) is selected in this equations, instead of selecting the zero vectors duringadaptive switching of the inverter:

If f ¼ 1 and t ¼ 1 then Vk1 ¼ nþ 1 and Vk2 ¼ nþ 2

Else if f ¼ 1 and t ¼ 0 then Vk1 ¼ n2 1 and Vk2 ¼ n2 2

Else if f ¼ 0 and t ¼ 1 then Vk1 ¼ nþ 2 and Vk2 ¼ nþ 1

Else if f ¼ 0 and t ¼ 0 then Vk1 ¼ n2 2 and Vk2 ¼ n2 1

if Vk . 6 then Vk ¼ Vk 2 6

if Vk , 1 then Vk ¼ Vk þ 6

9>>>>>>>>>>>=>>>>>>>>>>>;

: ð6Þ

3.2 Flux and torque estimatorThe torque and flux estimator block shown in Figure 1 utilizes equations (7)-(11) toestimate flux and torque values at m sampling period as follows (Gulez et al., 2007):

cDðmÞ ¼ cDðm2 1Þ þ ðVDðm2 1Þ2 RsiDÞTs ð7Þ

cQðmÞ ¼ cQðm2 1Þ þ ðVQðm2 1Þ2 RsiQÞTs ð8Þ

cs ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffic2D þ c2

Q

q; ls ¼ tan21 cQ

cD: ð9Þ

And, the reflected flux position is calculated as:

rs ¼ lsmod 60 ð10Þ

where: the stationary D-Q axis average voltage and current components are calculatedas follows:

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VDðm2 1Þ ¼ðVDk1tk1 þ VDk2tk2Þ

Ts;

VQðm2 1Þ ¼ðVQk2tk1 þ VQk2tk2Þ

Ts; ð11Þ

iD ¼ðiDðm2 1Þ þ iDðmÞÞ

2;

iQ ¼ðiQðm2 1Þ þ iQðmÞÞ

2;

where: the time pair set (tk1, tk2) are adaptive time set provided by the ANN control box.The torque value can be calculated using estimated flux values as:

Te ¼3

2PðCDðmÞiQðmÞ2CQðmÞiDðmÞÞ ð12Þ

3.3 The design of the ANNIn this study, an application of adaptive ANN-based controller for PMSM underhigh-performance DTC to minimize the torque ripples is presented. In this system,the stator flux position, and errors due to deviations from reference values of the torqueand stator flux are used to select two active vectors while at the same time the absolutevalue of the torque error, flux error and the stator flux position are used in ANN blockto adapt the switching of the inverter in order to control the applied average voltagelevel in such a way to minimize the torque ripples.

The proposed neural network “ANN” is shown in Figure 2.The symbols in the figure are:. IW – input layer weights;. LW – hidden layer weights; and. b – bias.

The NN structure in Figure 2 is formed four layers as one input, two hidden andone output layers. Input and output layers include 3 and 2 nodes while hiddenlayers include 5 and 5 nodes, respectively. In the first hidden layer, it is used asigmoid function for input data sets to recognize the system while in the secondhidden layer, it is used a linear function to let the results coming from the first

Figure 2.The simulated ANN

structure3 5 5 2

IW{1,1}

b{1} b{2} b{3}

LW{2,1} LW{3,2}

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hidden layer to output. The input layer accepts the absolute normalized torqueerror, normalized flux error as well as normalized reflected flux position. Theprocessing of the proposed ANN results in adaptive switching time for the twoselected vectors Vk1 and Vk2. It should be noted that such timing set has beencalculated in the literature (Swierczynski et al., 2002; Tang et al., 2004) for spacevector modulation in terms of PI controller parameters and stator resistance whichgives inaccurate results during operations. However, in this method the actualperformance can be compared with the required performance and online adjustmentof the weights may be achieved through back propagation based on the minimumof the sum of squared errors.

The trained weights of the ANN are obtained by training the proposed ANN offlinewith data selected from simulation of highly performance DTC of PMSM (Gulez et al.,2007). It is obtained a training set by entering the determined reference input values asinitial ones to operate the system. ANN structure is trained with this training set.Thus, the system variables have been learned by the trained ANN controller. Theinput data set include ðDTen;Dcsn; rnÞ and the output data set is (tk1, tk2). The offlinetraining takes 36 epochs to reach the required goal of the generalized system error asshown in Figure 3. The desired outputs and goals of the system are produced in testphase of NN controller for the related control signal.

4. Simulation and resultsThe control system shown in Figure 1 is simulated with MatLab software with IGBTinverter. Part of the simulation is supported with experimental results. The selectedsampling time is 100ms for the simulation and 120ms for the experimental work,the parameters of the simulated motor are given in Table I.

Figure 3.Training performance

100

10–1

10–2

10–30 10 20 30

36 EpochsStop Training

Tra

inin

g-B

lue

Goa

l-B

lack

Performance is 0.00179542, Goal is 0.0018

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4.1 Torque dynamic responseThe torque dynamic response with HDTC and the ANN-based DTC are shown inFigures 4 and 5, respectively. The reference torque for both algorithms is changed fromþ2.0 to 22.0 and then to 3.0 Nm with 0.05ms for each torque level. As shown in thefigures, the response with the proposed algorithm follows the reference torque withsmall torque ripples (,0.1 Nm) while the HDTC has higher ripples (,2 Nm).In addition, the torque response with the proposed algorithm shows fast response asthe HDTC response.

Number of pole pairs, P 2Stator winding resistance, Rs 5.8Vd-Axis inductance, Lsd 44.8 mHq-Axis inductance, Lsq 102.7 mHPermanent magnet flux, CF 533 mWbInertia constant, J 0.000329 Nms2

Friction constant, B 0.0Table I.

PMSM parameters

Figure 4.Dynamic torque response

with HDTC

4

3

2

1

0

–1

–2

–30 0.05 0.1 0.15

Time (s)

Torq

ue (

Nm

)

Figure 5.Dynamic torque response

with ANN-based DTC

4

3

2

1

0

–1

–2

–30 0.05 0.1 0.15

Time (s)

Torq

ue (

Nm

)

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4.2 Steady state responseThe steady state simulation is carried out with closed speed loop of 70 rad/s asreference speed and 2 Nm as load torque.

The motor performance results under steady state are shown in Figures 6-11.Figures 6 and 7 show the phase currents of the motor windings under HDTC and theproposed ANN-based DTC, respectively, observing the change of the waveform underproposed method, it is clear that the phase currents approach sinusoidal waveformwith almost free of current pulses shown in Figure 6.

The torque response in Figures 8 and 9 show considerable reduction in torqueripples with the applied new algorithm from 3.2 Nm (max. -to- max.) down to less than0.12 Nm when the new method is used, which in turn, reflected in smoother speedresponse as shown in Figure 11 compared to Figure 10.

Figure 6.Three phase line currentsof HDTC

3

2

1

0

–1

–2

–30 0.05 0.150.1

Time (s)

Cur

rent

(am

ps)

Figure 7.Three phase line currentsof ANN-based DTC

4

2

0

–2

–40 0.1 0.2 0.3 0.4

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Figure 8.Steady state torque

response of the HDTC

5

4

3

2

1

00 0.1 0.2 0.3 0.4 0.5

Time (s)

Torq

ue (

Nm

)

Figure 9.Steady state torque

response of theANN-based DTC

5

4

3

2

1

00 0.1 0.2 0.3 0.4

Figure 10.Motor speed response of

the HDTC

80

70

60

50

40

30

20

10

00 0.1 0.2 0.3 0.4 0.5

Time (s)

Spee

d (r

ad/s

)

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4.3 Noise and harmonics levelThe status of the low-frequency harmonics and EMI noise contained in the currentswith the HDTC and ANN based DTC are shown in Figures 12-15.

In Figure 12, the spectrum of phase-a current with HDTC shows that largelow-frequency harmonic current components with total harmonic distortion (THD) of,20.13 percent have widely distributed. They normally result in parasitic torqueripples components which in turn leads to mechanical vibrations and acoustic noise.

Figure 11.Motor speed response ofthe ANN-based DTC

80

60

40

20

00 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Figure 12.Line current (upper)and it is spectrum (lower)of HDTC

4

2

0

–2

–40.05 0.1 0.15 0.2 0.25

Time (s)

FFT windows 5 cycles of selected signal

50

40

30

20

10

0

Mag

(%

of

Fund

amen

tal)

0 500 1,000 1,500Frequency

2,000

Fundamental = 1.26, THD = 20.13%

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When the proposed algorithm is used, the THD is effectively reduced to be 2.66 percentand most of the low-frequency harmonics have disappeared (Figure 13).

The EMI noise level with the traditional HDTC in Figure 14 shows a noise level of,20 dB near operating frequency, ,18 dB near switching frequency (5 KHz) and240 db level for the most high-frequency components (.0.2 MHz), when the proposedalgorithm is simulated, the EMI noise level is damped down to , 2 20 dB nearoperating frequency (zero crossing point), , 2 30 dB near switching frequency andaround 260 dB level for the most high-frequency components as shown in Figure 15.

Figure 13.Line current (upper) and it

is spectrum (lower) ofANN-based DTC

80

60

40

20

00 2,000 4,000 6,000 8,000

Frequency (Hz)

Mag

(%

of

Fund

amen

tal)

Fundamental (22.26Hz) = 1.318, THD=2.66%

2

0

–20.1 0.15 0.2 0.25 0.3

FFT window: 5 of 7.677 cycles of selected signal

Time (s)

Figure 14.EMI noise level with

HDTC

20

0

–20

–40

–60

–80

0 0.1 0.2 0.3 0.4 0.5Frame: 3,810 Frequency (MHz)

Mag

nitu

de, d

B

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4.4 Experimental resultsThe test board system consists of PMSM-DC coupled motor, power module PS11035,current and voltage sensors, AD7862 unit, auxiliary power unit, and host computer.The host computer, which is used to carry out the real-time Cþþ algorithm consiststhe industrial digital input/output PCI decision card. The measured steady state torquewhich support the simulation results in Figures 8-9 are shown in Figures 16-17 for theHDTC and the proposed ANN-based DTC, respectively.

The oscilloscope figures of the line current for the two methods are shown inFigures 18 and 19, respectively. Apart from errors due to measurement pulses, the linecurrent response for the proposed method (Figure 19) is approaching sinusoidalwaveform with relatively minimum current pulses compared to HDTC line currentresponse shown in Figure 19. However, it is observed that for same DC level voltagethe speed of the motor with HDTC is faster than that of the proposed system.

5. ConclusionsA neural network-based controller for PMSM under DTC algorithm is proposed tominimize the torque ripples associated with HDTC. In this system, the stator flux

Figure 15.EMI noise level with theANN based DTC

20

0

–20

–40

–60

–80

0 0.1 0.2 0.3 0.4 0.5Frequency (MHz)

Mag

nitu

de, d

B

Figure 16.The motor torque of theHDTC

3

2

1

0

–10.01 0.03 0.05 0.07 0.09

Time (s)

Torq

ue (

Nm

)

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position, stator flux error and developed torque error are used to select two active vectorswhile at the same time the absolute value of the normalized torque error, the absolutevalue of the normalized stator flux error and the normalized stator reflected flux positionare used in neural network algorithm to adapt the switching of the inverter in order to

Figure 18.The line current of the

HDTC

Figure 17.The motor torque for the

proposed DTC

3

2

1

0

–10.01 0.05 0.1 0.15

Time (s)

Torq

ue (

Nm

)

Figure 19.The line current of the

proposed DTC

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control the applied average voltage level in such a way to minimize the torque ripples.The simulated results show considerable torque ripple and EMI reduction as well ascurrent harmonics reduction. The simulated results are supported with someexperimental results.

References

Buja, G.S. and Kazmierkowski, M.P. (2004), “Direct torque control of PWM inverter-fed ac motors– a survey”, IEEE Trans. Ind. Elect., Vol. 51 No. 4, pp. 744-57.

Cao, X., Zang, C. and Fan, L. (2006), “Direct torque controlled drive for permanent magnetsynchronous motor based on neural networks and multi fuzzy controllers”, paperpresented at International Conference on Robotics and Biomimetics ROBIO’06, Kunming,December 17-20, pp. 197-201.

Chung, S-K., Kim, H.S. and Youn, M-J. (1998), “A new instantaneous torque control of PMsynchronous motor for high-performance direct-drive applications”, IEEE Transactions onPower Electronics, Vol. 13 No. 3, pp. 388-400.

Dan, S., Weizhong, F. and Yikang, H. (1998), “Study on the direct torque control of permanentmagnet synchronous motor drives”, ICEMS 2001, Proceeding of The 15th InternationalConference on Electrical Machines and Systems, August 18-20, pp. 571-4.

Gulez, K., Adam, A.A. and Pastaci, H. (2007), “A novel direct torque control algorithm for IPMSMwith minimum harmonics and torque ripples”, IEEE/ASME Trans. on Mechatronics,Vol. 12 No. 2, pp. 223-7.

Li, S., Lizhi, S., Zhizhong, Z. and Yongping, L. (1998), “Compensation of ripple torque ofinverter-fed PM synchronous motors”, Proceedings of the American Control Conference,Chicago, Illinois, June 2000, pp. 1597-601.

Liu, T., Husain, I. and Elbuluk, M. (1998), “Torque ripple minimization with on-line parameterestimation using neural networks in permanent magnet synchronous motors”, IEEEThirty-Third IAS Annual Meeting, Vol. 1, October 12-15, pp. 35-40.

Luukko, J. (2000), “Direct torque control of permanent magnet synchronous machines – analysisand implementation”, PhD thesis, Lappeenranta University of Technology, Lappeenranta.

Martins, C., Roboam, X., Meynard, T.A. and Carylho, A.S. (2002), “Switching frequencyimposition and ripple reduction in DTC drives by a multilevel converter”, IEEE Trans. onPower Elect., Vol. 17 No. 2, pp. 286-97.

Mobarakeh, B.N., Tabar, F.M. and Sargos, F.M. (2000), “A self-organizing intelligent controllerfor speed and torque control of a PMSM”, IEEE Industry Applications Conference 2000,Vol. 2, October 8-12, pp. 1283-90.

Swierczynski, D., Martin, P.K. and Frede, B. (2002), “DSP based direct torque control ofpermanent magnet synchronous motor using space vector modulation”, IEEE ISIE, Vol. 3,May 26-29, pp. 723-7.

Tang, L., Zhong, L., Fazlur, R.M. and Hu, Y. (2004), “A novel direct torque controlled interiorpermanent magnet synchronous machines drive with low ripple in flux and torque andfixed switching frequency”, IEEE Trans. on Power Elect., Vol. 19 No. 2, pp. 346-54.

Tan, Z., Li, Y. and Li, M. (2001), “A direct torque control of induction motor based on three levelinverter”, IEEE PESC’ 2001, Vol. 2, pp. 1435-9.

Vas, P. (1998), Sensorless Vector and Direct Torque Control, Oxford University Press,New York, NY.

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Xiaodong, Z., Jiang, W. and Huiyan, L. (2004), “Torque ripple minimization in PM synchronousmotors based on back stepping neural network”, Proceedings of the 5th World Congress onIntelligent Control and Automation, Hangzhou, June 15-19, pp. 4422-6.

Yi, Y., Vilathgamuwa, D.M. and Rahman, M.A. (2003), “Implementation of an artificial neuralnetwork based real time adaptive controller for an interior permanent-magnet motordrive”, IEEE Transaction on Industry Applications, Vol. 39 No. 1, pp. 96-104.

Zhang, C., Ma, B., Liu, H. and Chen, S. (2006), “A new approach to direct torque control of interiorpermanent magnet synchronous motor”, paper presented at Systems and Control inAerospace and Astronautics, ISSCAA 2006, January 19-21, pp. 912-4.

Zhong, L., Rahman, M.F., Hu, W.Y. and Lim, K.W. (1997), “Analysis of direct torque control inpermanent magnet synchronous motor drives”, IEEE Trans. on Power Elect., Vol. 12 No. 3,pp. 528-36.

About the authorKayhan Gulez was born in Istanbul, Turkey, in 1970. He received BS, MS, and PhD degrees in allElectrical Engineering, from Yildiz Technical University, in Istanbul-Turkey, in 1992, 1995, and1999, respectively. He firstly worked as a research assistant in the Department of Electrical andElectronics Engineering, Engineering Faculty at Celal Bayar University, Manisa, Turkeybetween 1994 and 1997. Then, he joined the Department of Electrical Engineering, Electrical andElectronics Faculty at Yildiz Technical University in the July of 1997. Between 1997 and October1999, he continued to carry out his studies at the same department. He worked as a ResearchAssociate in a JSPS project and other short-term projects in Keio University and in TokyoMetropolitan Institute of Technology between October 1999 and November 2002. Currently, he isworking for the Department of Electrical Engineering at Yildiz Technical University, since hewas appointed as an Assistant Professor in March 2003. His major research interests areartificial neural networks and control applications, control of electric machines, control systems,EMC and EMI control methods, active, passive and EMI filter design methods and applicationsfor EMI noise and harmonic problems on which he has over 150 scientific papers and technicalreports in various journals and conference proceedings. He has also six science grand awardsbetween 1998 and 2007 in Yildiz technical University in Istanbul, and two best paper awardsfrom SCI’2001 and M&S’2001. Kayhan Gulez can be contacted at: gulez@yildiz.edu.tr

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