Direct Torque Control With ANN Speed Controller Based on Kalman Filter for PMSM

7/28/2019 Direct Torque Control With ANN Speed Controller Based on Kalman Filter for PMSM

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Direct Torque Control with ANN Speed Controller

based on Kalman filter for PMSM

F.Hamidia, MS.Boucherit A.Larabi M.Bouhedda

Ecole Nationale Suprieure Polytechnique Universit de Science et de Technologie Universit Yahia feres de Mdad'Alger, 10, avenue Pasteur, Hassan Badi, Houari Boumediene, B.P: 32, Bab-Ezzouar, Avenue de lALN, Ain Dheb,

BP 182, El Harrach, Alger, Algrie. 16111, Alger, Algrie. Mda, Algrie

Abstract this paper presents the application of Artificial Neural

Network (ANN) based on Kalman filter to replace PI speed

controller for PMSM using direct torque control (DTC).

Simulation results show that the proposed controller can provide

better speed control performance than those obtained by the

application of a conventional PI controller.

Keywords-Artificial Neural Network, PMSM, DTC, Kalmanfilter.

I. INTRODUCTIONThe Proportional Integral (PI) controllers are intensively

used in control application due to its versatility, discharges

benefits and facility of implementation.

However, PI controller is slow in adapting to speed

changes, load disturbances and parameters variations without

continuous tuning of its gains [1]

In the past, AC drives were only used in small demanding

applications, regardless of the advantages of AC motors as

opposite to DC motors, since the high switching frequency

inverters cost was rather competitive. With the developmentsin the power electronics area, the vector control methods,

which use fast microprocessors and digital signal processing

(DSP), made possible the use of induction motors in typically

DC motors dominated areas, since the current components

producing torque and flux are decoupled, achieving the system

separately excited DC motor similar features.

The Direct Torque Control (DTC) method, developed by

German and Japanese researchers, allows direct and

independent electromagnetic torque and flux control, selecting

an optimal switching vector, making possible fast torque

response, low inverter switching frequency and low harmoniclosses [2].

Even though the DTC technique was originally proposedfor the induction machine drive in the late 1980s, its concept

has been extended to the other types of ac machine drives

recently [3], as such Permanent Magnet Synchronous Motor.

A conventional PI speed controller has been used in motioncontrol applications for a long time [4]. Fuzzy logic and neuralnetworks has been a subject of growing interest in recent years

[5].Numerous works reported in recent past have shown that a

fuzzy logic controller has a potential to replace the

conventional PI controller. Fuzzy logic (FL) control

apparently offers a possibility of obtaining an improvement in

the quality of the speed response, compared to PI control [4],

but in order to increase the response time period of the system,

this paper proposes artificial neural network speed controller

and to improve the performance of direct torque control of

PMSM in closed loop, under transient and steady state

uncertainties caused by the variation in load torque, this neuralnetwork speed controller is based on Kalman filter.

II. MODEL PMSMThe transformation of PARK brings back to the equation

stator in reference frame related to the rotor.

= + = + + (1)Where Rs is the stator resistance, Id is the d-axis current,

Md is the total flux in the d-direction, Mq is the total flux in the

q-direction, and Iq

is the q-axis current. Flux-linkage can also

be expressed in dq coordinates as follows:

= += (2)Where Ld is the d-axis inductance, Mf is the flux-linkage

due to the permanent magnets, and Lq is the q-axis

inductance. As d-axis is aligned with magnets axis, there is

no contribution of the magnets to q-axis magnetic flux-

linkage Mf.

The motor torque expression with dq magnitudes is [6]:= (3)III. DIRECT TORQUE CONTROL PRINCIPLE

The basic idea of the DTC concept, whose block diagram is

shown in Fig. 1, is to choose the best vector of the voltage,

which makes the flux rotate and produce the desired torque.

During this rotation, the amplitude of the flux rests in a pre-

defined band. [7][8].

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Fig1. Schematic diagram of DTC-PMSM control with Speed Controller

The three-phase two level voltage source inverter creates

six non-zero voltage vectors, shown in Fig. 2, and two zero

voltage vectors, which can be applied to the machine terminals

[7][8].

The stator flux vector can be estimated using the measured

current and voltage vectors [7]:

= (1)Or = ( ) (2)

Then, the torque can be calculated using the components

of the estimated flux and measured currents:

= 32 (3)Where p is the pole pair and and represent the

Concordia transformation components of the current and flux.

Circular trajectory of the stator flux is divided into six

symmetrical sectors referred to as the inverter voltage vectors,

shown in Fig. 2. For each section, based on the torque and flux

errors, a proper vector set is proposed. Four switchingsolutions can be employed to control the torque according to

whether the stator flux has to be reduced are presented in

reference [9][10] cited by [11][12]. Switching method D is

used in this study.

Fig2. Spatial voltage vectors as function of the state inverter

The typical DTC includes two hysteresis controllers, one

for torque error correction and one for flux linkage error

correction. The hysteresis flux controller makes the stator fluxrotate in a circular fashion along the reference trajectory for

sine wave ac machines as shown in Fig. 3. The hysteresis

torque controller tries to keep the motor torque within a pre-

defined hysteresis band [3].

Fig3. Vectors Selection corresponding to stator flux amplitude control

At every sampling time the voltage vector selection blockdecides on one of the six possible inverter switching states (Sa,Sb, Sc) to be applied to the motor terminals.

The possible outputs of the hysteresis controller and the

possible number of switching states in the inverter are finite,

so a look-up table can be constructed to choose the 4appropriate switching state of the inverter. This selection is a

result of both the outputs of the hysteresis controllers and the

sector of the stator flux vector in the circular trajectory [3].

In Table I is presented the DTC selection algorithm.

TABLE I. SWITCHING TABLE

Flux Torque N=1 N=2 N=3 N=4 N=5 N=6 Controller

cflx=1

ccpl=1 V2 V3 V4 V5 V6 V1 Two

Levelsccpl=0 V7 V0 V7 V0 V7 V0

ccpl=-1 V6 V1 V2 V3 V4 V5 Three levels

cflx=0 ccpl=1 V3 V4 V5 V6 V1 V2 Two

Levelsccpl=0 V0 V7 V0 V7 V0 V7

ccpl=-1 V5 V6 V1 V2 V3 V4 Three levels


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IV. NEURALNETWORK SPEED CONTROLLER BASED ONKALMAN FILTER

To get better performance, a neural network controller has

been introduced in this paper to replace the traditional speed

controller PI.

Fig4. Schematic diagram of ANNs

This neural speed controller has one input; the error which

represents the difference between the speed command and ther output of the process, one neuron as output (whichrepresent torque command).

By using Matlab/Simulink toolbox, we use feedforward

algorithm, we select fourteen neurons at the hidden layer, with

the activation functions are 'tansig' for all neuron layers. Thesum squared error falls under 0.05 after 500 iterations.

The Kalman filter is proposed to training off-line our ANNs

speed controller.

V. DIGITAL SIMULATION RESULTSThe simulation results consider the variations in the load

and speed requirements. The system is first tested under a step

change with nominal in the speed reference (1200 rpm) and

constant load torque (2N.m) applied between (0.4sec and

0.6sec) as shown fig5.

We note that the estimated values of fluxes, torque and

rotor speed converge very well to their simulated values.The simulation results of speed and torque responses of the

motor showing in fig5 operate with PI and ANN speed

controller. It appears that, stator flux vector describes a

trajectory almost circular and the decoupling between flux and

torque is maintained

Fig.5 Performance of DTC using Artificial Neural Network (ANN) controller and Conventional controller (PI)


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To study the drive performance with a change in the

command speed, the system is tested under a speed reference

step change from 70 rad/sec to 125.66 rad/sec at t=0.5sec as

shown in fig6 and fig7b and an another test is performed by

applying a speed reversal command -125.66 rad/sec at t=2sec

is shown in fig9.

As shown in Fig. 6, 7 (represent Responses of torque androtor speed with zoom). ANNC still shows faster dynamics

and reaches the command speed in 0.045s with negligible

steady state error compared with 0.055s and steady state error

for PI scheme.

The motor reaches the reference speed rapidly and without

overshoot, load disturbances are rapidly rejected and the

efficiency, performance and reliability of PM synchronous

motor drive increases by the use of ANNC rather than that of

using PI Controller.

Fig.6. Electromagnetic torque response (comparison between PI and ANN controller)

Fig.7. Rotor speed response (comparison between PI and ANN controller)


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Fig. 8 Performance of Direct torque control with artificial neural network (ANN) speed controller (with change in speed command)

VI. CONCLUSIONDTC of the PMSM can be as attractive as DTC of an

induction motor. In this paper, a comparison study between PI

and ANN Speed Controller based on Kalman filter using DTC

of PMSM in closed loop are presented.The simulation results show that the PMSM drive systemhas faster response, smaller overshoot and better robustness

with application of artificial neural network speed controller

ANNSC compared to the classical controller PI.

REFERENCES

[1] Mutasim Nour, Omrane Bouketir, Chng Eng Yong, Self-Tuning of PISpeed Controller Gains Using Fuzzy Logic Controller, Modern AppliedScience, Vol 2, No 6,2008.

[2] Mohammed T. Lazim, Muthanna J. M. Al-khishali,, Ahmed Isa. Al-Shawi, Space Vector Modulation Direct Torque Speed Control OfInduction Motor, The 2nd International Conference on AmbientSystems, Networks and Technologies, Procedia Computer Science 5,pp.505512, 2011.

[3] Salih BarisOzturk, Direct torque control of permanent magnetsynchronous motors with non-sinusoidal back-EMF, Submitted to theOffice of Graduate Studies of Texas A&M University in partialfulfillment of the requirements for the degree of Doctor of Philosophy,may,2008.

[4] Zulkifilie Ibrahim, Fuzzy logic control of PWM inverter-fed SPMSMdrives, PhD Research Project, Liverpool JohnMoores University, 2000.

[5] Jagadish H. Pujar, S. F. Kodad, Robust Sensorless Speed Control ofInduction Motor with DTFC and Fuzzy Speed Regulator, InternationalJournal of Electrical and Electronics Engineering 5:1, pp.17-26, 2011.

[6] H.M. Hasanien, Torque ripple minimization of permanent magnetsynchronous motor using digital observer controller, EnergyConversion and Management 51, pp.98104, 2010.

[7] S.Kaboli M. R. Zolghadri, A. Emadi, A fast flux search controller forDTC based induction motor drives, Power Electronics SpecialistsConference, 2005. PESC05. IEEE36th, pp.739-744, 2005.

[8] S. Kaboli M. R. Zolghadri, D. Roye J. Guiraud J-C. Crebier, Design andImplementation of a Predictive Controller for Reducing the TorqueRipple in Direct Torque Control Based Induction Motor Drives, 200435th Annual IEEE Power Electronics Specialists Conference, Aachen,Germany, pp.476-481, 2004.

[9] I. Takahashi and T. Noguchi, A new quick-response and high-efficiency control strategy of an induction motor, IEEE Trans. onIndustry Applications, vol. 22, no. 5, pp. 820-827, 1986.

[10] Aaltonen, M., Tiitinen, P., Lalu, J. & Heikkila, S.. Direct torque controlof AC motor drives.ABB Review,3(95), pp.19-24, 1995.

[11] S. Kaboli, M. R. Zolghadri, P.Eskandari and D.Roye, Predictionalgorithm for torque ripple reduction in DTC-based drives, IranianJournal of Science & Technology, Transaction A, Vol. 31, No. A4,

pp.343-355, 2007.[12] S. Kaboli, M. R. Zolghadri, S. Haghbin and A. Emadi, Torque Ripple

Minimization in DTC of Induction Motor Based on Optimized FluxValue Determination, Industrial Electronics Society, 2003. IECON03.The 29th Annual Conference of the IEEE, Vol 1,pp.431-435.2003

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Direct Torque Control With ANN Speed Controller Based on Kalman Filter for PMSM