2011_Speed estimation of vector controlled squirrel cage asynchronous motor with artiﬁcial neural networks

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Speed estimation of vector controlled squirrel cage asynchronous motor

with artificial neural networks

Yuksel Oguz *, Mehmet Dede

Department of Electrical Education, Faculty of Technical Education, Afyon Kocatepe University, Afyonkarahisar, Turkey

a r t i c l e i n f o

Article history:Received 5 May 2009

Received in revised form 17 December 2009

Accepted 30 July 2010

Available online 21 August 2010

Keywords:

Vector control

Asynchronous motor

Speed estimation

Artificial neural networks

a b s t r a c t

In this paper, the artificial neural networks as a sensorless speed estimator in indirect vector controlledsquirrel cage asynchronous motor control are defined. High dynamic performance power semi conduc-

tors obtainable from direct current motors can also be obtained from asynchronous motor through devel-

opments in digital signal processors (DSP) and control techniques. With using of field diverting control in

asynchronous motors, the flux and moment can be controlled independently. The process of estimating

the speed information required in control of vector controlled asynchronous motor without sensors has

been obtained with artificial neural networks (ANN) in this study. By examining the data obtained from

the experimental study concluded on the DSP application circuit, the validity and high performance of the

ANN speed estimator on real-time speed estimation has been demonstrated.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

AC motors have been used as the workhorse in industry appli-cations due to their simple construction, high robustness, reliabil-

ity, low price and high efficiency for many years. AC motor drives

have been widely used in many of industrial and process applica-

tions requiring high performances. The vector-control technique,

which is based on the field orientation principle, has been widely

used in industry for high-performance control of AC motor drives

[1,2]. In the industrial applications, different vectorial control

methods are being used. Conventional vector control methods re-

quire motor speed as a feedback signal. Transducers such as

shaft-mounted tachogenerators, resolvers, or digital shaft position

encoders are used to obtain the real-speed information. The vector-

control technique is easy to implement and independent of ma-

chine operation conditions. The basic idea of the field-oriented

control (FOC) algorithm is to decompose a stator current into flux

and torque producing components. Both components can be con-

trolled separately after decomposition. The structure of the motor

controller is then as simple as that for a separately excited DC mo-

tor. [3,4]. The aim of vector control is to implement control

schemes that produce high dynamic performance and are similar

to those used to control DC machines. The control performance

of the AC motor drives depends on mechanical parameter varia-

tions, external torque disturbances, resistance changes, measure-

ment noise, frictional variations, and system uncertainty for

improper field orientation in transient state [58].

In recent years, it is possible to find a number of works that deal

with both sensorless direct and indirect vector control methods for

induction motors (IMs). These methods could be developed withreference to the rotor flux, stator flux or airgap flux; however,

the rotor oriented control allows the independent control of flux

and torque [9]. Using vector control, it is possible to achieve the

speed and torque control of IMs both in the transient and steady-

state. For the indirect vector control of IMs, accurate knowledge

of the slip frequency is required in addition to the rotor speed.

On the other hand, the direct vector control of IMs requires infor-

mation on the amplitude and position of the flux with reference to

the stationary stator axis, with the addition of the rotor angular

speed for speed control [9,10].

The motor speed is measured by using taco generator on enco-

der. The flux of AC motors can be directly measured by using flux

bobbins and flux sensors, even if it is difficult. These flux and speed

sensors cause decrease in mechanical durability, increase in cost,

noise and decrease in system security [11]. Additionally, in very

high speed and forceful applications, placement of such sensors

is difficult. On account of these reasons, researches in induction

motor drives have been focused on the elimination of speed sensor

at the motor shaft without deteriorating the dynamic performance

of the drive control system.

Many speed-sensorless control methods for IMs have been

developed [1219]. Speed-estimation techniques based on the

standard smooth-airgap induction machine model hence cannot

work at zero electrical frequency. For this problem solution, at-

tempts have been made to estimate speed by injecting high-

frequency carrier signals into the stator currents or voltages

[20], but these schemes are based on either second-order effects

0196-8904/$ - see front matter 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.enconman.2010.07.046

* Corresponding author.

E-mail address: [email protected] (Y. Oguz).

Energy Conversion and Management 52 (2011) 675686

Contents lists available at ScienceDirect

Energy Conversion and Management

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http://dx.doi.org/10.1016/j.enconman.2010.07.046mailto:[email protected]://dx.doi.org/10.1016/j.enconman.2010.07.046http://www.sciencedirect.com/science/journal/01968904http://www.elsevier.com/locate/enconmanhttp://www.elsevier.com/locate/enconmanhttp://www.sciencedirect.com/science/journal/01968904http://dx.doi.org/10.1016/j.enconman.2010.07.046mailto:[email protected]://dx.doi.org/10.1016/j.enconman.2010.07.046


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or a specially modified rotor structure. The speed-estimation tech-

niques based on injected carrier signals and the fundamental

smooth-airgap induction machine model second-order effects or

a specially modified rotor structure have been presented in

[13,21,22]. Various control algorithms for the elimination of the

speed sensor have been proposed: algorithms using state equa-

tions [23], model reference adaptive systems [24], Luenberger- or

Kalman-filter observers [25], saliency effects [26], sliding-modecontrols [27], artificial intelligence [28,29], sensorless vector con-

trol [30], direct controls of torque and flux [31], nonlinear inverter

model and parameter identification [32]. These algorithms are

mainly based on the flux and speed estimations. which are ob-

tained from the terminal electrical quantities, and they are compli-

cated and have difficulties in the speed estimation. The proposed

sensorless vector controlled scheme cannot require speed estima-

tions, and directly uses stator current and voltage.

The speed can be estimated by using space vector angular fluc-

tuation (SVAF) signal for inverter-driven induction motor in Ref.

[33]. This speed estimation algorithm can be used to estimate

the motor speed in real time without a speed sensor. This algo-

rithm needs two stator current signals and employs DSP tech-

niques to filter and manipulate the speed-related harmonics. In

the sensorless speed control of induction motors with direct field

orientation, the rotor flux and speed information are dependent

on the observers. However, the exact values of the parameters that

construct the observers are difficult to measure and changeable

with respect to the operating conditions. The adaptive sliding-

mode flux and speed observer is improved to make flux and speed

estimation according to parameter variations. The effects of

parameter deviations on the rotor flux observer can be reduced

by the interaction of the current sliding-mode observers [27].

Ref. [34], presents a novel unit to estimate the speed and the rotor

resistance for induction motor drives. This unit is based on a new

Adaptive Linear Neuron (ADALINE) structure, which is suitable

for single output systems only [34]. In Ref. [7], a recursive least-

squares estimator and Kalman estimators are developed to esti-

mate parameters, flux, and speed for vector-controlled inductionmotor drives. The recursive least-squares estimator is based on

the continuous time induction motor model in a stationary two-

axes reference frame. Estimation errors of this developed method

lie below a 6% value. In Ref. [10], a speed-sensorless indirect

field-oriented control for induction motors based on high-gain

speed estimation is designed. For this purpose, a new high-gain

speed estimator is realized on basis of the torque current regula-

tion error. This method has the achievable dynamic performances

of the speed sensorless controller for induction motors.

The artificial neural networks (ANN) attract more attention in

control of nonlinear systems like in estimation of speed. ANNs have

been used in some power electronic applications, such as inverter

current regulation [35], DC motor control [36], flux estimation

[37], speed estimation [38] and observer-based control of inductionmachines[39]. Studieson ANN are directedto two main fields;to de-

velop new models and theories related to functions of human brain

andapply the theoriesto real problemsin theworld. Thoughthere is

a mutual correlation between these two research fields, application

of ANN in solution of problems of which solution are difficult with

conventional methods and are uneconomical is very important.

In this study, the process of estimating the rotor speed informa-

tion required in control of vector controlled asynchronous motor

without sensors has been obtained with artificial neural networks

(ANN). First, measurements ofq-axis current (Iq) rotor speed andq-axis voltage (Vq) rotor speed are made for the rotor speed esti-

mation. Then the ANN the rotor speed estimator is utilized to esti-

mate the rotor speed. With data obtained from the study made on

the DSP application circuit, performance of the speed estimator hasbeen examined. The experimental results show a fast response and

accurate performance of the proposed method in estimation the

rotor speed for vector controlled squirrel cage asynchronous

motor.

2. Vector control of field-oriented squirrel cage asynchronous

motor

Squirrel cage asynchronous motors are simple, durable, mainte-nance free and the cheapest motors in all powers. Besides, disad-

vantages such the electrical and mechanical noise caused by

commutator brush system in direct current motors, continuous

maintenance obligation and not to be able to work in explosive

mediums do not exist in asynchronous motors. For this reason,

vector controlled asynchronous motors will make using of direct

current motors in sensitive servo control applications unnecessary

in the future by means of fast developments in microelectronic

field. Even in applications that do not require high dynamic perfor-

mance, it will be preferred to conventional methods with regard to

reliability and energy saving [40].

The vector control method was firstly applied by Blaschke and

many researchers from various countries like Leonhard and Bose

contributed in development of that method. There are mainly fourtypes of vector control methods depending on options of superpo-

sition of reference axis on magnetization flux, rotor flux, stator flux

or rotor during decomposition of stator current to its moment and

flux components [40].

Besides, direct and indirect control methods are defined accord-

ing to obtaining style of unit vectors used in transformations. The

main problems experienced in application of such methods are:

flux measures not sensitive enough, control system just depended

on motor parameters and deterioration of decoupling feature of

flux components [41].

Because of the magnetic coupling between the stator and rotor

phases of three-phase asynchronous motor, modeling of its dy-

namic behavior in the three-axis system is possible with variable

coefficient differential equations that change in time and a very

complex model structure appears. For this reason, dynamic behav-

ior of an asynchronous motor fed by a balanced three-phase

frequency converter is modeled in a two-axis system consists of

dq axes [1]. In that system, parameters that change in time are

eliminated and all the parameters and variables are defined on

orthogonal d and q axes decoupling from each other.

The dynamic model of the machine can be defined in constant

or rotating axis systems. In the constant axis system, ds and qs ref-

erence axes are in constant position by stator. In the rotating axis

system, ds and qs reference axes rotate in rotor speed or synchro-

nous speed. The advantage of the axis system rotating in synchro-

nous speed is that the variables are constant by time [42].

In general, the control is realized according to the following

steps:

Measuring of motor current and voltage magnitudes.

By applying the Clarke transformation, a b transferring ofthese magnitudes to the two-phase system.

Calculation of rotor flux vector and angular position.

By using the Park transformation, transferring of stator currents

d to q reference plane.

Controlling of moment component (isq) and flux component (isd)

individually from stator currents.

Calculation of reference voltage values of vq and vd.

By using the revert Clarke transformation, obtaining ofva, vb, vc.

By using the space vector modulation (SVM), production of

three-phase output voltage.

Transformations used during the process in vector control ofthree-phase squirrel cage asynchronous motor are given in Fig. 1.

676 Y. Oguz, M. Dede / Energy Conversion and Management 52 (2011) 675686


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In voltage equations used to analyze the dynamic performance

of an asynchronous motor, it is seen that some inductances ofasynchronous motor change in time. In other words, these induc-

tances are a function of rotor cycle. Coefficients in differential

equations change in time except when the rotor stops. Complexity

of differential equations can be decreased by changing (defining)

variables with other variables. This process in defined as transfor-

mation. In general transformations, the real variables of the

machine can be defined on other reference plane. Generally, this

plane is an arbitrary reference plane. All the known transforma-

tions can be obtained from such general transformations. For this,

it will be sufficient to only know the rotating speed of the reference

plane to be used. In Fig. 2, vectorial presentations related to refer-

ence planes are given.

2.1. The Clarke transformation

The space vector, with two axes of (ab), can be transferred toother reference plane. The a axis and a axis can be shown bybeing considered in the same direction in the vector diagram given

in Fig. 3.

The projection transforming the three-phase system to two-

dimensional system (ab) is given in (1) and (2):

ia i2 1

ib 1ffiffiffi3

p ia 2ffiffiffi3

p ib 2

2.2. The Park transformation

This transformation is the most important part of the vector

transformation. In reality, this transforms the projection to a

two-phase system on dq rotating reference plane. If we consider

that the d axis is adapted with the rotor axis, the diagram shows

correlation between two reference planes for the current vector.

(Fig. 4).

Whereas h is the rotor flux position. The flux and moment com-

ponents of the current vector are expressed with (3) and (4)

equations:

Fig. 1. Vector control transformations in the three-phase system [43].

Fig. 2. (a) Three-phase reference plane, (b) two-phase reference plane, and (c) rotating reference plane.

isi

ia,

b

c

Clarkeb(c)

a

Fig. 3. The Clarke transformation.

Y. Oguz, M. Dede / Energy Conversion and Management 52 (2011) 675686 677


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id ia cos h ib sin h 3iq ia cos h ib sin h 4

These components depend on components in the current vec-

tors (ab) and rotor flux position. If the correct flux position isknown, on that projection, dq elements will be constant. Here, a

two coordinated system independent from time of which id (flux

component) and iq (moment component) and direct moment con-

trol is possible and easy has been obtained.

2.3. The inverse Park transformation

After controlling, to transform two voltage vectors (vd, vq) ob-

tained on the d, q plane to three-phase motor voltage, first of all,

we must transform it from two-axis arbitrary reference plane to

two-axis constant reference plane (va, vb). For this process, firstly

the reverse Park transformation (5) and (6) is used (Fig. 5).

Va Vd cos h Vq sin h 5Vb Vd sin h Vq cos h 6

2.4. The inverse Clarke transformation

The next step is the transformation from two-axis constant ref-

erence plane (va, vb) to three-phase reference plane (va, vb, vc). Forthis process, expressions in the reverse Clarke transformation (7)

(9) are used. (Fig. 6).

va vb 7

vb vb ffiffiffi3

pva

28

vc vb ffiffiffi3

pva

29

3. The vector control methods

The vector control can be applied in two ways; the direct vector

control and indirect vector control. In the direct vector control, po-

sition of the rotor flux is directly measured with sensors. In the

indirect vector control, the rotor speed and sliding speed is used

and there is no need for a special structure. They can only be cal-

culated by using speed feedback. In vectorial control, choosing of

moment expression of the machine is very important. The stator

current and stator flux or moment expressions created by the rotor

flux will be chosen as moment expressions. In this method, stator

current can be easily obtained and there is only the flux formation

problem. If the flux involved in then moment expression can be

kept constant (stator or rotor flux), moment can only be controlled

linearly with component of stator flux on the q axis. During that

control, the flux must not change just as on the free stimulating di-

rect current machine. This can only be realized through a control to

be realized on the rotor flux. When the moment control is made

through stator by keeping the stator flux constant, as the flux will

be affected from that control, no linear control occurs. When the

rotor flux is kept constant, the correlation between the q axis stator

current and moment is linear. Thus, the stator current components

that control the flux and moment are vertical to each other and can

be controlled independently from each other. There is no magnetic

interaction between them [10,25]. A vector controlled block dia-

gram is given in Fig. 7.

3.1. The direct vector control method

In the direct vector control, the flux is determined with directmeasuring. In the measuring method, the flux sensors placed in

the air gap, flux bobbins placed specially on the stator or observer

models are used. It is disadvantageous if the flux sensors are af-

fected from the heat or are fragile.

3.2. The indirect vector control method

In the direct vector control, in formation of unit vectors, some

difficulties are experienced. In the indirect vector control, the

angular position of the rotor flux vector can be determined by

means of calculation by considering the principle of ids, iqs couples

define only one sliding angular frequency [40]. By using the motor

equations and speed feedback, the effective value of voltage, its fre-

quency and phase related control magnitudes are established.

4. Synchronous reference frame (dq) dynamic model of

squirrel cage asynchronous motor

The dq transformation that ensures analyzes of electric ma-

chines on any reference frame and is suggested by R.H Park is de-

fined as in (10)(12). In Fig. 8, the dynamic model equivalent

circuit of the squirrel cage asynchronous motor on a synchronous

plane is given

Iqd0s SuIabcs 23

cosu cos u 2p3

cos u 2p

3

sinu sin u 2p

3

sin u 2p

3

1

2

1

2

1

2

264

375

I2bcs

10

is

i

i

q

Parki

iiqid

iqid

d

Fig. 4. The Park transformation.

Fig. 5. The inverse Park transformation.

Fig. 6. The inverse Clarke transformation.



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where

Iqd0s iqs ids i0sT 11

Iabcs ias ibs icsT 12

u is an angle between iqs and ias.By using the (2.56) equation, the mathematical model of asyn-

chronous motor on synchronous reference frame is obtained as in

(13)(16):

veqs Rsieqs ddtweqs xedsweds 13

veds Rsieds

d

dtweds xeqsweqs 14

veqr 0 Rrieqr

d

dtweqr xe xrwedr 15

vedr 0 Rriedr

d

dtwedr xe xrweqr 16

where vqs, vds are the stator voltages, vqr, vdr the rotor voltages,

wqr, wdr the rotor flux linkages, xe the synchronous speed, xr theElectrical rotor speed, Ls the stator inductance, Rs the stator resis-

tance, Lr the rotor inductance degraded to stator, Rr is the rotorresistance degraded to stator.

Fig. 7. Block diagram of vector-controlled induction motor [44].

+ - +-

(a)

+- + -

(b)

Fig. 8. Synchronous plane: (a) qe-axis and (b) de-axis dynamic model equivalent circuit.



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Stator in these equations can be written as given in (17)(19) in

terms of rotor air gap flux linkages current.

weqs

weds

weqr

wedr

266664

377775

Ls 0 Lm 0

0 Ls 0 Lm

Lm 0 Lr 0

0 Lm 0 Lr

26664

37775

ieqs

ieds

ieqr

iedr

266664

377775

17

weqm Lmieqs ieqr 18

wedm Lmieds iedr 19The electrical moment produced by motor can be obtained on

synchronous reference frame as shown in (20):

Te 3PLm4Lr

wedrieqs weqrieds 20

As a result of rotor field diverting on the synchronous reference

frame, as the qe axis rotor flux linkages is zero (weqr = 0), the electri-

cal moment and synchronous speed can be determined as given in

(21) and (22) by simplifying the above stated equations:

Te 3PLm4Lr

wedri

eqs 21

xe xr Lmsrwdr

iqs 22

By using the mechanical circuit of motor, the motor speed and

its position can be determined as given in (23):

dxrdt

P2J

Te TL; dhrdt

xr 23

Transformation from synchronous frame axes to constant frame

axes and from here, to three-phase voltages can be obtained is gi-

ven in (24)(28). Transformations between planes are given vecto-

rially in Figs. 9 and 10.

vsqs veqs cos he veds sin he 24

vsds veqs sin he veds cos he 25

vas vsqs 26

vbs 12

vsqs

ffiffiffi3

p

2vsds 27

vcs 12

vsqs

ffiffiffi3

p

2vsds 28

In variables at the Eq. (25)(28), e indicates the synchronous

reference frame and s indicates the constant reference frame.

5. Artificial neural networks

ANNs are successfully used in a lot of areas, such as control,early detection of electrical machine faults, and digital signal pro-

cessing in everyday technology. The memory of a neural network

lies in the weights and biases. Neural networks can be classified

into three categories according to how the weights and biasesFig. 9. Transformation between dsqs and deqe planes.

Fig. 10. Ones transformed to asbscs and dsqs plane.



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are obtained: fixed-weight, unsupervised and supervised networks

[28,29]. In this paper, supervised networks are used. The set-up for

a supervised network is shown in Fig. 11. In the supervised net-

work, the weights and biases are adaptively trained by a learning

mechanism, which has been the mainstream of neural model

development. The most popular learning algorithm is known as

back-propagation. The best initial weights and biases for back-

propagation networks are created at random, utilizing the mini-

mum and maximum value of each input. The jth weight-update

equation of the ith neuron is given as Eq. (29):

Wijt 1 wijt @Em@wijt

29

Here g is the learning rate, wij(t+ 1) is the new weight, and wij(t) is

the old weight.

6. Experimental system set-up and the proposed speed

estimation method

In this study, it is aimed to estimate the speed information of

vector controlled squirrel cage asynchronous motor without using

a sensor by means of ANN. For this purpose, a motor control devel-

opment card by Microchip Company is used. In the following para-

graphs, dsPIC micro processor, control card and a three-phase,

50 Hz, 0.55 kW, 2-pole squirrel cage asynchronous motor (ASM)used for that purpose will be introduced. The block diagrams of

vector controlled squirrel cage asynchronous motor and system

components are given in Fig. 12.

The label information for the squirrel cage asynchronous motor

used in application is given in Table 1.

6.1. Digital signal processor (DSP) programming and motor control

card

The programming card used in the application is a set devel-

oped by the MPLAB PM3 Microchip Company for dsPIC applica-

tions. The dsPIC can be programmed from a computer by means

of the USB interface on that device and data in its memory can

be easily transferred to the computer. In Fig. 13, LEDs, buttons,

LCD screen and external programming outputs are given.

On the control card, there are two trimpots, LCD screen, LEDs

and buttons. Other than these, there are external power source

and communication port outputs. The motor control card and

power module used in the application are shown in Fig. 14.

6.2. Introduction of the control system software

The program realizing the control is edited and inspected after itis entered in C languageby means of MPLAB IDEprogram andloaded

in the microprocessor by MBLAP PM3 programming device via USB

line. Data saved in the microprocessor by means of MPLAB IDE pro-

gram are transferred to the computer medium. Thesoftware used in

the application is software belongs to MICROCHIP Company.

In general, the program uses the current and voltage data from

analog inputs and the speed information from encoders as input

data. It forms a new PWM by using the vector control algorithm

and sends it to the power module. The program flow diagram is gi-

ven in Fig. 15.

w Input

Output

Target

+

ANN

Fig. 11. A supervised network.

Computer

Programming Card(MPLAB PM3)

Control Card(dsPIC 30F6010) Power Module

ASM3-phase

Fig. 12. Block scheme of the application circuit.

Fig. 13. MPLAB PM3 programming device.

Table 1

Label information for the asynchronous motor.

GAMAK 4404354284

3 $ MOT TYPE AGM71 2b EFF

I.CL F IP 55 B3 S1 CE

V Hz A kW Cos u r/min

D 220 2.3

Y 380 50 1.34 0.55 0.84 2780



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6.3. Obtaining of data on vector controlled squirrel cage asynchronous

motor

The vector controlled asynchronous motor in no load position is

operated in 100 cycle increases starting from speed of 500 r/min to

speed of 1000 r/min. In each operation, the Speed-q-axis current

(Iq) and Speed-q-axis voltage (Vq) data couples are taken. To save

these data in the processor, the commend lines are added in thesoftware. To start saving, it is obligatory to press any button. While

the system is operating, vector rotates in control mode with 480 r/

min speed. After the button is pressed at any time and the first 500

data are saved, the speed automatically increases at any desired

rate and other 500 data are saved. The sampling time of these data

is 4 ms.

From Speed-Iq and Speed-Vq data obtained from the motor oper-

ated in different speed phases, total 50 data groups are obtained.

By taking means of these Vq and Iq data, four data groups are ob-

tained related to change depending on speed. The Iq and Vq change

during 800 r/min increase of the motor speed and correlation be-

tween these changes are given in Fig. 16.

When means of these data are taken (obtained depending on

the correlation given in Fig. 15), linear graphics are obtained. The

change related to these obtained data is given in Fig. 17.

As it can be understood from the graphics in Fig. 17, there is a

linear correlation between the speed and Vq and Iq parameters. In

this figure, four data groups are obtained. With one group of these

data, ANN is trained and with other group, tests are made. Data

used in training are given in Table 2.

Data used in training are given in Table 2.

The data to be used for ANN tests are taken from the 2nd data

group. Ten data are chosen for the test. These data are given in

Table 3.

7. Modeling of system with artificial neural networks

The used ANN model is a multi-layer perceptron model wheremore than one layers are used between its input and output layers.

Algorithm of error backward diffusion is used as the training algo-

rithm. The error backward diffusion algorithm is a coded algorithm

that minimizes the error function (of which square is taken) and is

used to train the generalized delta rule.

The training of that ANN model is shown is the flow diagram in

Fig. 18. According to the flow diagram in Fig. 18, the training pro-

gram of ANN is entered in C++ programming language.

Modeling of the system with ANN consists of four phases as

follows:

1. Obtaining of input and output data of the system.

2. Choosing of ANN structure.

3. Realization of training process.4. Conformity test of ANN model of the system.

The ANN parameters modeling the system are given below:

1. The input number is 2.

2. The output number is 1.

3. The layer number is 1.

4. The cell number in layer is 4.

5. The layer activation function is Sigmoid.

6. Maximum iteration number is 500,000.

7. The learning coefficient is 0.7 .

8. The momentum coefficient is 0.9.

As there is not any definite criterion in choosing of the layernumber in ANN structure and cell number in each layer, the layer

Measurement of speed ( ) and phasecurrent (i ,i ,i )

a b c

(i , i , i ) (i , i )a b c d q

Calculation of rotor flux vector angularposition

New transformation angle calculation

(V -V ) (V -V )q d

(V -V ) (V , V ,V ) a b c

Fig. 15. Program flow scheme.

Fig. 14. motor control development card.



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Fig. 16. Momentarily Iq and Vq change depending on speed.

Fig. 17. Changes of averaged: (a) Vq and (b) Iq parameters depending on speed.



10/12

number and cell numbers are determined by means of trial and er-

ror method. Similarly, the learning and momentum coefficients are

determined depending on the experiences in previous studies. The

input data is normalized to 5. The output is between 0.1 and 0.99.

Change of error between the ANN structure and training process

is given in Fig. 19. As the quadratic error decreases to 0.0009 in

500,000 iteration, it has been decided to cease training.

After the training process with ANN is completed, the data ta-

ken from the 2nd group are tested. The obtained results and real

values are given in Table 4 and Fig. 20.

As it can be seen in Table 4 and Fig. 20, the speed information

estimated with ANN application is close to its real values and they

are correct. As these parameters are used in vector control, there is

no need to use any additional element. By observing the change of

these two data in the software program, the speed is estimated in

correct manner.

8. Experimental results and discussion

In this study, to prove the correctness of the proposed ANN

speed estimator, an experimental set that belongs to vector con-

trolled squirrel cage asynchronous motor drive was used. In the

experimental study, the motor controlled development card used

for dsPIC applications is MPLAB PM3. In Table 1, the label values

of three-phase and 2-polar squirrel cage asynchronous motor used

in the experimental study are given.

The vector control algorithm and the speed estimation algo-

rithm are executed by the motor control board with a DSP chip.

MPLAB PM3 program uses q-axis current (Iq) and q-axis voltage

(Vq) from analog inputs and the real-speed information from qua-

dratic encoder as input data. The real speed measurement is also

made by the DSP. By running in various speed phases, rotorspeed-Iq and rotor speed-Vq of the squirrel cage asynchronous mo-

tor were taken as given in Table 2. In this study, the speed range of

squirrel cage asynchronous motor was taken at 5001000 rpm.

After the study, it was determined that the speed information

required for recycling in vector control could be obtained with

ANN speed estimator by using the q-axis current (Iq) and q-axis

voltage (Vq) parameters. In Table 3, the rotor speed, q-axis current

(Iq) and q-axis voltage (Vq) values selected in order to be used in the

ANN test and approximate speed values obtained with the ANN

speed estimator were compared. The variation of error in training

process with ANN is given in Fig. 19. As it was seen that the qua-

dratic error decreased to 0.0009 in 500,000 iteration, the training

made with ANN was ceased. As matter of fact, as it is seen in

Fig. 20, the speed information estimated with the ANN speed esti-mator are very close to its real values and are correct. As these

parameters were used in vector control, there was no need to

use an additional component. By tracing the change of these two

data in software, correct estimation of speed was realized. Besides,

by only using the data that belong to Iq and Vq parameters, without

using the real rotor speed, the same speed estimation values were

obtained with the ANN speed estimator.

Estimators, observers and spectral analysis methods are fre-

quently used techniques for sensorless speed estimation of induc-

tion motors estimators depend on accurate machine model and

parameter estimation in model reference adaptive system. How-

ever, the induction motors are nonlinear and their parameters vary

with time and operating conditions. Observers and spectral analy-

sis method have a relatively long delay and data processing timethat can limit real-time speed measurement [33,4548]. As matrix

Table 2

Data used for training of ANN.

Speed (r/min) Vq Iq Speed (r/min) Vq Iq

500 10696.1 1163.13 750 15442.36 1269.61

510 10913.26 1167.74 760 15368.38 1299.24

520 10978.29 1168.72 780 15935.34 1288.33

530 11102.31 1172.81 790 16342.53 1321.67

550 11615.22 1209.52 810 16714.49 1363.19

560 11829.56 1195.33 820 16889.13 1319.54570 11779.87 1173.65 830 17029.32 1334.25

590 12237.28 1202.06 840 17059.48 1357.85

610 12707.98 1207.65 850 17292.82 1359.06

620 13156.5 1218.07 860 17538.94 1306.12

630 12988.33 1270.22 880 17758.64 1364.38

640 13398.96 1267.22 890 18074.85 1318.5

660 13635.37 1247.71 900 17844.09 1355.26

670 13623.12 1257.44 910 18472.37 1351.47

680 13930.32 1240.82 920 18572.41 1310.43

690 14368.58 1271.96 930 19029.92 1343.77

700 14547.13 1266.25 940 18919.34 1364.04

720 14751.32 1263.9 960 19215.21 1371.74

730 14880.76 1310.61 970 19394.23 1350.02

740 15232.35 1303.01 1000 20283.67 1432.19

Table 3

Data used in the ANN test.

Speed (r/min) Vq Iq Speed (r/min) Vq Iq

510 10925.76 1132.77 760 15366.56 1320.98

540 11381.67 1180.65 870 17606.56 1363.98

580 12104.86 1187.31 910 18273.08 1344.23

610 12804.96 1261.86 970 19343.09 1355.52

670 13793.27 1263.88 1000 20203.5 1412.27

Determine starting valuesof very weights random

Apply training set to neurolnetworks

Find error from network outputand desired output, and add it to

total error

Update weights withbackpropogation of error

Is trainingset completed?

Is error smallsufficiently?

Take training setto beginning

Fig. 18. The flow diagram of ANN training program entered in C++ language.



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operations are used intensively in such applications, the time per-

iod 15 times of the cycle period of the all vector controlled algo-

rithm is needed. Extension of estimation period and sampling

times in speed estimation operations of asynchronous motors is

not a desired situation. The period to reach to desired speed value

in real speed estimation methods and techniques of induction mo-tors is shortened with the ANN speed estimation algorithm real-

ized in this study.

9. Conclusions

In this study, a sensorless speed estimation algorithm with ANN

has been successfully demonstrated for a sensorless indirect vector

controlled squirrel cage asynchronous motor drive system. Data

selected for the ANN test was used to evaluate the capabilities of

the proposed ANN speed estimator for real-time speed estimation.

The performance of ANN speed estimator is found to be excellent

in the wide speed region. Although the estimator performance is

demonstrated for a sensorless vector controlled squirrel cage asyn-

chronous motor drive system, it can also be used to scalar or vectorcontrol of drive systems. The proposed ANN speed estimator can

improve the performance and reliability of squirrel cage asynchro-

nous motor drives, because it does not require a speed sensor, ex-

tra wiring and detailed machine model.

Fig. 19. Change of error during the training.

Table 4

Values of motor speed estimated with ANN and its real values.

The obtained

results with

ANN

As a speed up equivalent

with ANN achieved data (r/

min)

The

real

data

The real data as a

speed equivalent (r/

min)

0.126076 514.64 0.1178 510

0.169747 539.18 0.1712 540

0.246175 582.12 0.2424 580

0.321902 624.66 0.2958 610

0.403430 670.46 0.4026 670

0.557564 757.05 0.5628 760

0.766499 874.43 0.7586 870

0.822947 906.15 0.8298 910

0.924769 963.35 0.9366 970

0.994801 1002.69 0.99 1000

Fig. 20. Values of motor speed estimated with ANN and real motor speed values.



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2011_Speed estimation of vector controlled squirrel cage asynchronous motor with artiﬁcial neural networks