Review of Control Methods (Autosaved)

8/10/2019 Review of Control Methods (Autosaved)

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%i&.!1# %lu' and ((% in )" drive

The V" usually separates current into

!eld and torque producing components.

The perpendicular !eld system ma'es

the relationships between the machine

variables simple, in principle. The (ux isa function of the !eld /producing

component1 or daxis current, the

torque is proportional to the product of

this (ux and the torque /producing

component1 or qaxis current. f the (ux

is established and can be held constant,

the torque response is governed by the

current and can be fast and well-

controlled.

ull advantages of V" are given only if

the instantaneous position of the rotor

(ux vector can be established. The

usual % cast cage rotor aids in

robustness and economy, but rotor

quantities are not accessible.

ig. /21 3loc' diagram of the -S# with direct

rotor (ux orientation

%i&. !3#*loc+ dia&ram of the S)

with indirect rotor u' orientation

Precise speed and torque control of an

induction motor is now possible due to

the recent developments in power

electronics and digital signal processors

/#SP1. #ynamic characteristic of an

induction motor can be controlled using

!eld oriented control technique. Thistechnique can be classi!ed into two

methods. The rst methodis 'nown as

direct !eld orientation%i&. !#. t uses

Hall sensors mounted in the air gap to

measure the machine (ux, and

therefore obtain the (ux magnitude and

its angle for !eld orientation. The

second method is 'nown as indirect

!eld orientation%i&. !3#. t uses the

rotor speed to achieve (ux orientation

by imposing a slip frequency derived

from the rotor dynamic equations, %i&.

!4#llustratedphasor diagram of indirect

!eld orientation.

ndirect !eld orientation method is

generally preferred than the direct one.

This is because direct method requires

a modi!cation or a special design for

the machine. %oreover the fragility of

(ux sensors often degrades the

inherent robustness of an inductionmotor drive[, 3, 8].


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%i&. !4#/hasor dia&ram e'0lainin&indirect eld orientation

)irect 2orue "ontrol !)2"##T" also exploits vector relationships,

but replaces the coordinate

transformation concept of standard V"

with a form of bangbang action,

dispensing with P$% current control

/3u4a and 5a)mier'ows'i, 26671. In

standard VC the q-axis current

component is used as the torque

control quantity. With constant rotorux it directly controls the torque. n a

standard 8 phase converter, simple

action of the 9 switches can produce a

voltage vector with : states, 9 active

and 2 )ero. The voltage vector and

stator (ux then move around a

hexagonal tra4ectory& with sinusoidal

P$% this becomes a circle%i&. !#.

$ith either, the motor acts as a !lter,

so rotor (ux rotates continuously at

synchronous speed along a nearcircular trac'. n #T" the bangbang or

hysteresis controllers impose the time

duration of the active voltage vectors,

moving stator (ux along the reference

tra4ectory and determining duration of

the )ero voltage vectors to control

motor torque. -t every sampling time

the voltage vector selection bloc'

chooses the inverter switching state to

reduce the (ux and torque error.

#epending on the #T" switching

sectors, circular or hexagonal stator(ux vector path schemes are possible.

#T" has these features compared to

standard V" /3u4a and 5a)mier'ows'i,

26671;

< =o current control loops so current

not directly regulated

< "oordinate transformation not

required

< =o separate voltage P$%

< Stator (ux vector and torque

estimation required

#epending on how the switching

sectors are selected, 2 di+erent #T"

schemes are possible. >ne, proposed

by Ta'ahashi and =oguchi /0?:91,

operates with circular stator (ux vector

path and the second one, proposed by

#epenbroc' /0?::1, operates with

hexagonal stator (ux vector path

/Ter)ic and @adric, 26601. There are

di+erent types of #T" schemes as /3u4aand 5a)mier'ows'i, 26671;

< Switchingtable based #T" /ST#T"1

< #irect Self "ontrol scheme /#S"1

< "onstant switching frequency #T"

scheme

3asically, the #T" strategies operating

at constant switching frequency can be

implemented by means of closedloop

schemes with P, predictive*deadbeator =eurou))y /=1 controllers. The

controllers calculate the required stator

voltage vector, averaged over a

sampling period. The voltage vector is

!nally synthesi)ed by a P$% technique,

which in most cases is the SpaceVector

%odulation /SV%1. Therefore, di+erently


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from the conventional #T" solution, in a

#T"SV% scheme the switching

harmonics are neglected in the control

algorithm[1-4].

%i&. !#.*loc+ dia&ram of )2" drive

s5stem

Sensorless"ontrol

"onventionally, a direct speed

sensor, such as a resolver or an

encoder, is usually mounted to the

motor shaft to measure its speed. The

use of direct speed sensor besides

being bul'y and reduces the robustness

of the overall system, it adds extra cost

to the drive system. Speed sensor, also,implies additional electronics, extra

wiring, extra space and careful

mounting which detracts from the

inherent robustness of the drive.

%any advantages are expected from

speedsensorless induction motor

drives;

Aeduced hardware complexity.

Bow cost.

Aeduced si)e.

Climination of direct sensor

wiring. 3etter noise immunity.

ncreased reliability.

Bess maintenance requirements.

Suitable for hostile environments,

including temperature.

#espite much e+ort and progress,

operation at very low speed is still

problematic particularly for an %

sensorless drive.[1-3]

There is a lot of literatures thatclassied speed-sensorless

methods, One of these literatures

classied speed-sensorlesssystems

according to rotor model, stator

model, parasitic properties and

MRA( adaptive, oservers, !"#,

$irect and A%% method&[16].

Other literature classied speed-

sensorless systems into the

follo'ing three categories;

0. %ethods based on detecting space

harmonics induced by slots. These

methods have the advantages of

being independent of machine

parameters and give high accuracy

of speed estimation at low speeds&

however, they need high precision

measurements which increase the

hardware*software complexity.

2. %ethods based on high frequency

signal in4ection into the motorwindings. The rotor position or the

(ux direction is identi!ed from the

current response to this

superimposed high frequency signal.

Bi'e the last category, these

methods are independent of

machine parameters and give high

accuracy of speed estimation down

to )ero speed& however, they also

need high precision measurements.

8. %ethods based on the machine

model and its terminal variables. n

these methods the motor

parameters along with its input

current and applied voltage are used

in di+erent ways to estimate the


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operating speed. These methods are

considered simpler than the previous

ones& however they still inaccurate

at low and )ero speed[,11].

1. Rotor Slot armonics (ethodsAotor slot harmonics methods of

speed estimation are based on

detecting space harmonics induced by

rotor slots. The rotor slots generate

space harmonic components in the air

gap magneto motive force /mmf1 that

modulate the stator (ux lin'age at a

frequency proportional to the rotor

speed, and to the number of rotor slots.

To isolate the signal that represents the

mechanical angular velocity of therotor, a band pass !lter is employed

having its center frequency adaptively

tuned to the rotor slot harmonic

frequency. The signal is shown in the

lower trace of the oscillogram of %i&.

!9#. -s mentioned earlier, this approach

needs high precision measurements

which increase the hardware*software

complexity.

The sensorless control methods utili)ing

the rotor slot harmonics, which are

caused by the structure of %, has been

proposed The speed information of slot

harmonics has high robustness for any

drive condition and motor parameter

deviation because the slot harmonics

are based on the structure of

%[16,1].

. Si&nal In:ection (ethodst is well 'nown within the community

of sensorless control that rotor positiondetection at very low speeds and at

standstill is only possible with signal

in4ection methods, because at

vanishing speeds the di+erent methods

using the induced voltage /bac' C%

methods1 are not suitable.

%i&.!9#S0eed estimation ;ased on

rotor slot harmonics

Speed estimation scheme as shown in

%i&. !7#is based on signal in4ection. -

high or low frequency voltage signal,

superimposed on the fundamentalvoltage, is typically used to excite the

anisotropic phenomena of the motor

and the rotor position or (ux direction is

identi!ed from the current response.

Signalin4ection methods based on

spatially anisotropic models have

several well'nown problems.

-nisotropies depend on the motor

design, and they are usually wea' in

standard induction motors. The signalcarrying useful information may be

distorted due to interference with other

signals of the same 'ind. urthermore,

the spatial variation of the lea'age

inductance depends on load and (ux

level, often leading to diDculties at high

loads. - common drawbac' of signal


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in4ection methods is that their dynamic

response is usually only moderate.

%i&. !7#S0eed estimation ;asedon si&nal in:ection

n S methods the machine is in4ected

with extra, low level signals usually at

high frequency. The much higher

frequency and low magnitude of the

in4ected signals result in the

fundamental behavior of the machine

being little changed. The in4ected

signals may be periodic or alternating in

a particular spatial direction. These

signals are modulated by theorientations of the machine

asymmetries, and are then processed

and demodulated to yield the required

measurement. Such asymmetries occur

more naturally in S%s [16,13,14].

liding mode oserver has proved

to e more roust than the model

reference adaptive speed oserver

'hen parameter variations of the

)M occur[].

3. (achine (odel (ethods- great deal of research interest is

given to the third category of speed

estimation, which is based on machine

model, for its simplicity. n this

category, the motor terminal variables

and its parameters are used in some

way to estimate its operating speed.

This category can be classi!ed

according to the algorithm used for

speed estimation. They include the use

of simple open loop speed calculation,%odel Aeference -daptive Systems

/%A-S1, adaptive (ux observer,

Cxtended 5alman ilters, arti!cial

intelligence techniques and sliding

mode observer[18].

)irect "alculation (ethods%i&.!8# illustrates the direct

calculation method for speed

estimation. -ssuming the motor

parameters are completely 'nown, theinstantaneous speed can be calculated

directly. The process of speed

estimation is illustrated in the bloc'

diagram. -s shown a rotor (ux

estimation process is essential for

speed calculation. This is the main

drawbac' of this method.

%i&.!8#*loc+ dia&ram of rotor s0eed

estimation structure

2here are three 0ro;lems related

to the rotor u' estimationalman lter structure

Ain et al -/0 summarizes the drabacs to a

con$entional 1*23

a) Costly costly calculation of 4acobianmatrices5

b) !iasbiased estimates5

c) "ynamics instability due to linearizationand erroneous parameters5

d) Assume hite 6aussian noise5

e) #uning lac of analytical methods for

model co$ariance selection.

They ad$ocate the %unscented$ *2, o$ercoming

some drabacs5 lo speed tests ere not

reported, since it can be more susceptible to

measurement noise[1#$!1%!5]

rticial Intelli&ence 2echniuesFig. 1&"Illustrates the structure of a speed

estimation algorithm based on Artificial 7eural

7etors (A77). It has to independent flu'

obser$ers5 the first defines the $oltage equations


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that do not in$ol$e

r

as a reference model and

the second defines the current equations in$ol$ing

r

as an ad#ustable model. The output of the

A77 is defined as the estimated speed

r)

, hich

is subsequently used as an input for the ad#ustable

model. If the estimated speed de$iates from the

real speed, an error occurs beteen the flu' from

the ad#ustable model

)r

and the flu' from the

reference model

r

. Then, the error is bac

propagated to the A77 and the eights of the

A77 are ad#usted online to reduce the speed

estimation error.

Methods based on A77 gi$es good speed

estimation hoe$er, they are relati$ely

complicated and require large computation

time[!'$!#].

Low speed operation is the main area where

difficulties arise. The problems can include:

a) Signal Ac%uisition &rrors These are a basic

limitation for $ery lo speed operation, minor 89

components in the signals used in () can produce

substantial offsets in the estimated flu' linage

e$en if a pure integrator could be used.

%i&. !13#Structure of the s0eed

estimation usin& ?eural ?etwor+

b) 'n(erter The in$erter introduces nonlinear

dead:time effects5 $ery good performance at lo

speed ill require compensation. 2urther

nonlinearities come from poer de$ice forard

$oltage drops and may also require modeling.

Additional effects include the sensiti$ity of$oltage drop and dead time compensation to the

e'act point of current re$ersal. 1stimating the

stator $oltage $ector from the +M inde' can

then become inaccurate.

c) Model arameters +arameters can be

determined in a commissioning phase, either

offline or using the in$erter to self:test aiding

accuracy of estimation.. This might include

finding a good initial $alue of the stator resistance

using a 89 test.[1(]

*revious methods of speed-

sensorless control ased on ho' to

measure or estimate speed, so you

have innite methods to achieve

this,depending on machine type,

parameters of the machine and the

speed region at 'hich you 'ant to

control+

ompleity of theAlgorithms

-lthough the basic schemes for the

sensorless control of ac machines are

essentially not extremely complex, their

reali)ation in industrial drives has to

face the requirementsthat arise in

di+erent environmentsand operating

conditions. Aegardlessof the chosen

scheme /with or withoutin4ection1, their

implementationin commercial drivesconsiders thefollowing aspects;

The automatic parameter tuning

and compensation of their

variations on function of current

and temperature.


10/18

The acoustic noise as well as the

additional ripple in current and

torque because of the in4ection. - suitable starting procedure in

case of S% drives.

The interaction with the standardcontrol.

a seamless switching between

control with and without sensors. The optional switching of operation

between the mode with sensor and

the one without sensor. -n encoder emulation based on the

calculated speed.

or many manufacturers, the

complexity as well as the quality of the

control is in a process of evolution

based on the experiences acquired in

new applications [4].

Intelli&ent

controllers

n recent years, scientists and

researchers have acquired signi!cant

development on various sorts ofcontrol theories and methods. -mong

these control technologies, intelligent

control methods, which are generally

regarded as the aggregation of fu))y

logic control, neural networ' control,

genetic algorithm, and expert system,

have exhibited particular superiorities.

The fu))y logic controller /B"1 method

can be utili)ed in systems that have

vagueness or uncertainty.

The main advantages of intelligent

controllers are; their designs do not

need the exact mathematical model of

the system and theoretically they are

capable of handling any nonlinearity of

arbitrary complexity. >ver the last

decade researchers have done

extensive research for application of

controllers for HPVS# systems.

Simplicity and less intensive

mathematical design requirements are

the main features of intelligent

controllers, which are suitable to dealwith nonlinearities and uncertainties of

electric motors [7-34].

Intelli&ent control techniues

%u@@5 Ao&ic "ontrol !%A"#

The development of fu))y logic was

motivated in large measure by the

need for a conceptual framewor'

which can address the issue of

uncertainty and lexical imprecision.Some of the essential characteristics of

fu))y logic relate to the following

/Eadeh, 0??21;

" n fu))y logic, exact reasoning is

viewedas a limiting case of

approximate reasoning.

" n fu))y logic, everything is a matter

ofdegree.

" n fu))y logic, 'nowledge is

interpreted acollection of elastic or,

equivalently, fu))yconstraint on a

collection of variables.

" nference is viewed as a process of

propagationof elastic constraints.

" -ny logical system can be fu))i!ed.

There are two main characteristics of

fu))y systems that give them better

performance for speci!c applications.

-u))y systems are suitable for

uncertain or approximate reasoning,especially for the system with a

mathematical model that is diDcult to

derive.

3 u))y logic allows decision ma'ing

with estimated values under

incomplete or uncertain information.


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#u$$y control is a methodology to

represent and implement a /smart1

humanFs 'nowledge abouthow to

control a system. - fu))y controller is

shown in %i&. !14#.the fu))y controllerhas several components;

The rulebase is a set of rules about

how to control.

u))i!cation is the process of

transforming the numeric inputs

into a form that can be used by the

inference mechanism.

The inference mechanism uses

information about the currentinputs /formed by fu))i!cation1,

decides which rules apply in the

current situation, and forms

conclusions about what the plant

input should be.

#efu))i!cation converts the

conclusions reached by the

inference mechanism into a

numeric input for the plant.

%i&. !14#*asic "on&uration of a

fu@@5 lo&ic s5stem

Another author descries the

principle fu..y control as follo's;

[37]0 Simple the output signal of the plant

2"alculate the error and change of

error 8 #etermine

the fu))y subset and membership

function for error and change of error

7 #etermine the change of control

action according to the individual fu))y

rule

G "alculate the actual change of

control action by defu))i!cation

operation9 Send the change of control action to

control the converter

Io to step 0

. $isadvantages

- simple fu))y controllerimplemented in

the motor drive speed controlhas a

narrow speed operation and needs

muchmore manual ad4usting by trial

and error if highperformance is wanted

[36-34].

rticial ?eural ?etwor+

!??#

Aecently, the reemerging arti!cial

neural networ' /-==1 techniques have

been widely applied in the !eld of

system identi!cation and control [3].

The capabilities of -==s for the

identi!cation and control of nonlinear

systems were investigated in depth by

=arendra and Parthasarathy[46].

%rticial neural networ&s are circuits,

computer algorithms, or mathematical

representations loosely inspired by the

massively connected set of neurons

that form biological neural networ's.

-rti!cial neural networ's are an

alternative computing technology that

have proven useful in a variety of

pattern recognition, signal processing,estimation, and control problems, %i&.

!1#shows a basic con!guration of a

neuralnetwor' system.

%rticial neural networ&semerged after

the introduction of simpli!ed neurons

by %c"ullochand Pitts in 0?78

/%c"ulloch J Pitts, 0?781.These


12/18

neurons were presented as models

ofbiological neurons and as

conceptualcomponents for circuits that

could performcomputational tas's. The

basic model of theneuron is founded

upon the functionality of abiologicalneuron. K=eurons are the basicsignaling

units of the nervous systemK and

Keachneuron is a discrete cell whose

several processesarise from its cell

bodyK[3-38].

%i&. !1#*asic "on&uration of a

?eural-?etwor+ s5stem

?euro-%u@@5 "ontrol !?%"#

The B" and -== have their own

advantages and drawbac's. n order to

get the advantages of both B" and

-==, researchers developed neuro

fu))y controller /="1 for motor drive

applications

=eurofu))y controllers /="s1%i&.

!19#, which overcome disadvantages of

fu))y logic controllers and neural

networ' controllers, have been utili)ed

by authors and other researchers for

motor drive applications.#espite many advantages of ="s, the

industry has been still reluctant to

apply these controllers for commercial

drives due to high computational

burden caused by large number of

membership functions, weights and

rules, especially on selftuning

condition. High computation burden

leads to low sampling frequency, which

is not suDcient for implementation [41-

49].

%i&. !19# Structure of ?ero %u@@5

"ontroller

"om0arison ;etween %A", ??

and ?%"

-mong the intelligent controller B"

is the simplest for speed control of high

performance P%S% drive. n contrast to

conventional control techniques, B" is

the best in complex illde!ned process

that can be controlled by a s'ill human

operator without much 'nowledge oftheir underlying dynamics. Aecently

researchers have wor'ed to develop

B"s for motor drives to mimic human

thin'ing as closely as possible. $or's

have already been reported on the use

of B"s for conventional dc motors,

switched reluctance motors and %

drives. The use of neural networ' in

control systems is very attractive

because of their ability to learn, to

approximate functions, to classify

patterns and their potential for

massively parallel hardware

implementation.

The conventional B" has a narrow

speed operation and needs much more

manual ad4usting by trial and error if


13/18

high performance is wanted. >n the

other hand, it is extremely tough to

create a serial of training data for -==

that can handle all the operating

modes. The more advanced intelligent

controller is =", which is thecombination of B" and -== controller.

The =" utili)es the transparent,

linguistic representation of a fu))y

system with the learning ability of

arti!cial neural networ's. Thus it ta'es

advantages of both B" and -==. They

used structure learning and parameter

learning. 3ut it is not suitable to

implement in industry because of a lot

of computational burden. urthermore,

they didnFt consider the (ux

control[41].

Benetic l&orithm

#uring the last thirty years there has

been a rapidly growing interest in a

!eld called 'enetic %lgorithms /I-s1.

However, if -== provides good results,

why re4ect themL t would be enough

to !nd a method that 4usti!es theoutput o+ered by -== based on the

input values. This method would have

to be able to be applied to networ's of

any type, meaning that they would

have to comply with the following

characteristics /Tic'le, 0??:1;

< )ndependence of the

architecture. The method for

extracting rules should be able to be

applied independently from the

architecture of the -==, including

recurrent architectures.

< )ndependence of the training

algorithm. The extraction of rules

cannot depend on the algorithm used

for the -==Fs learning process.

< orrection. %any methods for

extracting rules only create

approximations of the functioning of

the -==, instead of creating rules as

precisely as possible

represent the 'nowledge obtained

from the -== as eloquently as

possible.

To gain a general understanding of

genetic algorithms, it is useful to

examine its components. 3efore a I-

can be run, it must have the following

!ve components;

0. - chromosomal representation of

solutions to the problem.

2. - function that evaluates the

performances of solutions.

8. - population of initiali)ed solutions.

7. Ienetic operators that evolve the

population.

G. Parameters that specify the

probabilities by which these genetic

operators are applied[47].

%i&. !17# Roulette Cheel Selection

dvanta&es of B$

0they are derivativefree technique


14/18

2they can be used for both continuous

and discrete optimi)ation problems

8they use stochastic operators instead

of deterministic rules to search for an

optimum solution

7they consider many points in thesearch space simultaneously, not a

single point. Thus there is a reduced

chance of converging to local minima.

Gthey are ideal for parallel processor,

thus the operations can be speeded up.

Benetic l&orithm Ste0s

0-Random initiali.ation of

population;-n initial population is created

randomly or heuristically. n general,

there are n individuals /point in the

research space1 in the population and

even numbers of n. -n individual is

characteri)ed by a !xedlength binary

bit string, which is called a

chromosome. Cach of the string is

decoded into a set of parameters that is

represents. The initial population is then

a collection of randomly generated

individual binary string.1-!valuation of tness of

individuals in the population;n this step, all the individuals of the

initially created population are

evaluated by means of a !tness

/ob4ective or evaluation1 function /f1

/the function to be minimi)ed or

maximi)ed1. The !tness function is then

used in the next step, to create a

genetic pool.

2-%e' population generation&-fter evaluating the !tness of the

individuals of the initial population is

created, the creation of a new

generation is performed basically in

three stages, reproduction, crossover,

and mutation. The overall goal of this

step is to obtain a new population with

individuals which have !tness values.as

shown in %i&. !18#.

%i&. !18#Sim0lied ow chart of aBenetic l&orithm

The steps involved in creating and

implementing a genetic algorithm

are as follo's%i&. !17#;0Ienerate an initial, random

population of individuals for a !xed

si)e&

2Cvaluate their !tness&8Select the !tness members of the

population&7Aeproduce using a probabilistic

method /e.g. roulette wheel1&Gmplement crossover operation on the

reproduced chromosomes /choosing

probabilistically both the crossover site

and the MmatesN1&9Cxecute mutation operation with low

probability&

Aepeat step 2 until a prede!nedconvergence creation is met.(he convergence criterion of a genetic

algorithm is a user-specied condition

e.g. the maximum number of

generations or when the string tness

value exceeds a certain threshold.


15/18

00lications of B on control

s5stems[48,4]A genetic algorithm has many

applications in control systems,

'hich can e summari.ed in the

follo'ing points; In a fu$$y logic system, I- can be

used to search for the best number and

shape of membership function of fu))y

logic controller for a certain control

problem. t can also use tune the two

inputs and one output scaling factors of

madmantype fu))y controller. In a neural networ&, I- can be used to

search for appropriate architecture of a

neural networ'. t may be also to tune

the weights of the neural networ'. In a )I* controller, I- can be used to

turn the parameters+0, +i and +d of a

P# controller, to give the desired

response.

R=%=R=?"=S

1. $. Beonhard, K"ontrol of Clectrical

#rives,K 2nd ed. 3erlin, Iermany,

SpringerVerlag, 0??9.. @ohn $. inch, and #amian Iiaouris,

K"ontrolled -" Clectrical #rives,K CCC

Transactions >n ndustrial

Clectronics, Vol. GG, =o. 2, pp. 7:0

7?0, ebruary 266:.3. =alin 5ant %ohanty ,

Aanganath%uthu and %.

Senthil5umaran, K - Survey on

"ontrolled -" Clectrical #rivesK,

nternational @ournal of Clectrical and

Power Cngineering,Vol. 8, =o.8, pp.

0G0:8, 266?.

4. I. S. 3u4a and %. P. 5a)mier'ows'i,

K#irect torque control of P$%

inverterfed -" motors - survey,K

CCC Trans. nd. Clectron., Vol. G0, =o.

7, pp. 77OG, -ug. 2667.

. . 3lasch'e, KThe principle of !eldorientation as applied to the new

transvector closed loop control

system in a P$% inverter induction

motor drive,K Siemens Aev., Vol. 8?,

=o. G, pp. 20O226, 0?2.

9. %. S. Ea'y, KAobust Speed "ontrol of

Permanent %agnet Synchronous

%otor #rives,K n Proc. of 08th

nternational %iddleCast Power

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