IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 2, JUNE 2003 271

A Review of RFO Induction Motor ParameterEstimation Techniques

Hamid A. Toliyat, Senior Member, IEEE, Emil Levi, Senior Member, IEEE, and Mona Raina, Student Member, IEEE

AbstractAn induction motor is the most frequently usedelectric machine in high performance drive applications. Controlschemes of such drives require an exact knowledge of at leastsome of the induction motor parameters. Any mismatch betweenthe parameter values used within the controller and actualparameter values in the motor leads to a deterioration in the driveperformance. Numerous methods for induction machine onlineand offline parameter estimation have been developed exclusivelyfor application in high performance drives. This paper aims atproviding a review of the major techniques used for the inductionmotor parameter estimation. The paper is illustrated throughoutwith experimental and simulation examples, related to variousparameter estimation techniques.

Index TermsInduction motor drives, parameter offline identi-fication, parameter online estimation, vector control.

I. INTRODUCTION

F IELD oriented (or vector) control is the most popularac machine control method that is widely used in highperformance industrial applications of electric drives. In thecase of an induction machine, rotor flux oriented (RFO) controlrequires an accurate value of at least some of the motorparameters in order to yield robust control. Which parametersare required depends on the applied RFO control scheme. Ifthe applied parameter values within the control system donot match the actual values in the motor, detuned operationresults. Impact of parameter variations on various vector controlschemes has been studied in detail in the past and extensivediscussions are available in many books [1][5].

A vector controlled induction motor can be used within atorque drive, a speed drive, or a position drive. The type of thedrive that exhibits the highest sensitivity to the incorrect param-eter values is the torque drive. Although the motor parametervariations affect the speed control applications too, existenceof the PI speed controller considerably reduces negative con-sequences of the parameter detuning.

Induction motor parameters change with temperature, fre-quency, and saturation. The consequence of any mismatchbetween the parameter values used in the controller and thosein the motor is that the actual rotor flux position does not coin-cide with the position assumed by the controller. The situationis illustrated in Fig. 1, [4]. This means that the actual rotor

Manuscript received January 21, 2002.H. A. Toliyat and M. Raina are with the Department of Electrical En-

gineering, Texas A&M University, College Station, TX 77843-3128 USA(e-mail: toliyat@ee.tamu.edu; Mona@ee.tamu.edu).

E. Levi is with the School of Engineering, Liverpool John Moores University,Liverpool, L3 3AF, U.K. (e-mail: e.levi@livjm.ac.uk).

Digital Object Identifier 10.1109/TEC.2003.811719

Fig. 1. Illustration of commanded (d q ) and actual (dq) rotor fluxoriented reference frames in detuned operation, caused by a parametermismatch. Because the commanded reference frame does not coincide with theactual one, decoupled rotor flux and torque control does not take place.

flux contains both - and -axis component, leading to a lossof decoupled flux and torque control. Performance of the drivetherefore deteriorates from the desired. In order to avoid sucha situation, it is necessary to provide the vector controller withaccurate induction motor parameter values. These parametershave to be obtained somehow from measurements, during ini-tialization of the drive. Since any vector controlled inductionmotor drive is inverter fed, numerous tests based on an invertersupply have been developed in recent past for determinationof the required parameter values [4][7]. Such methods arefurther on called offline parameter identification methods.In addition, numerous possibilities exist nowadays to updatethe parameter values during the drive operation [3][7]. Thetechniques that enable parameter adaptation during the driveoperation are further on termed online parameter estimationmethods.

The aim of this paper is to provide a review of the major tech-niques used for the induction motor offline and online parameteridentification and estimation, respectively.

II. INDUCTION MOTOR PARAMETERS

The parameters that may need to be identified offline ortracked online depend on the vector control scheme underconsideration. If the drive operates with the constant ratedflux reference, the required parameters will be some or all ofthe following: rated magnetizing inductance, stator resistance,rotor resistance, and stator/rotor leakage inductance or transientstator inductance. If the drive operates with a variable flux

0885-8969/03$17.00 2003 IEEE

272 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 2, JUNE 2003

reference (optimal efficiency drives, operation in the fieldweakening region, etc.), magnetizing curve will usually berequired as well. Finally, if the drive controller includes somekind of compensation of the iron losses (that may be especiallyimportant for torque drives in electric or hybrid vehicles), onewill need to know the variation of the equivalent iron loss resis-tance with operating frequency [8]. The most important offlineidentification and online parameter estimation techniques arereviewed in the remainder of this paper.

III. OFFLINE PARAMETER IDENTIFICATION TECHNIQUES

It is often the case in practice that one manufacturer suppliesthe inverter with a vector controller, while the machine comesfrom another manufacturer. It is then not possible to set the pa-rameters of the controller in advance and these have to be setonsite, once when the inverter is connected to the machine. Sucha situation has led to the development of the so-called self-com-missioning procedures for vector controlled induction machines[9], [10]. The main idea behind this concept is that the controllerautomatically determines all of the parameters of an inductionmachine, required for vector control. The automated procedureof testing and calculation is done following the first enabling ofthe controller. As the induction machine may already be cou-pled to a load, the tests aimed at self-commissioning have toidentify the required parameters at standstill. The identificationis therefore performed with single-phase supply to the machine.In principle, two types of excitation may be applieddc or ac.The one ideal for true self-commissioning is dc. From applieddc voltage and resulting dc steady state current, one finds thevalue of the stator resistance. Determination of the remainingparameters is then based most frequently on transient current re-sponse that follows application of the dc voltage. Self-commis-sioning schemes that rely on this approach are those describedin [11][16].

The methods regarded as suitable for commissioning butinappropriate for self-commissioning are those that eitherrequire some special conditions to be satisfied during thecommissioning (for example, the machine is allowed to rotate)or they require substantially more complicated mathematicalprocessing of the measurement results, when compared tothe self-commissioning methods. For example, proceduresdescribed in [17][19] are all based on some tests withsingle-phase supply to the machine. However, the methoddescribed in [17] involves application of pseudo-randombinary-sequence voltage excitation and requires an adaptiveobserver. The procedure of [18] relies on maximum likelihoodmethod to obtain transfer function parameters. A step voltage isapplied at the stator terminals and the stator voltage and statorcurrent responses are recorded. The Laplace transformationis used to get the transfer function along with the maximumlikelihood estimation. The method of [19] requires applicationof the recursive least squares algorithm, this being the same asfor the procedure of [20].

The second possible excitation for parameter identificationat standstill is single-phase ac. Standstill frequency responsetest forms in this case the basis for the parameter identifica-tion [21][24]. A particularly interesting procedure based on

single-phase ac excitation is the rotor time constant identifica-tion method of [25]. It is based on trial-and-error and essentiallydoes not require any computations.

Some of the offline identification procedures surveyed so farenable identification of the machines magnetizing curve in ad-dition to other rated parameter values. Such is the case for themethods described in [13], [15], [21][23]. It should be notedthat the requirement for magnetizing curve identification oftenadds to the complexity of the commissioning procedure sincemore than one test needs to be performed. A significant stepforward in this sense is the method of [26], where magnetizingcurve is identified at standstill using only one test with single-phase ac supply. Other possibilities of the magnetizing curveidentification for self-commissioning purposes have been ex-plored in [27][30].

If the conditions of the commissioning are less stringent, thedrive may be allowed to rotate for the purposes of parameteridentification. A whole array of additional parameter determi-nation methods opens up in this case. For example, an extremelysimple procedure for rotor time constant tuning [31] is based onthe tests performed while the machine is rotating. The drive isoperated in the torque mode for the purposes of the rotor timeconstant tuning, with rated rotor flux reference. An alternatingsquare-wave torque reference is applied at certain speed of rota-tion. If the rotor time constant value used in the controller is cor-rect, the actual torque is an alternating square-wave as well, sothat the speed response follows a triangular function. If the rotortime constant setting is not correct, situation of Fig. 1 resultsand the actual torque response is not the same as the torque ref-erence. Speed response then deviates from triangular. An exper-imental illustration of this trial-and-error method of rotor timeconstant tuning is given in Fig. 2.

Standard no-load test and locked rotor test may be performedwith a PWM inverter supply if the commissioning situation al-lows for such testing. Parameters that are calculated are the sameas those obtained with sinusoidal supply, provided that the cal-culations are based on the fundamental components [32]. Thisfeature is exploited in [33], where the parameters are identi-fied using the dc, no-load and the pseudo-locked rotor tests. Amethod for pseudo-locked rotor test is presented since the me-chanical locking of the rotor is undesirable in any onsite com-missioning scenario.

Identification of the machines magnetizing curve becomesa simple and straightforward task if the machine is allowedto rotate under no-load conditions during the onsite commis-sioning. By defining the magnetizing curves analytical approx-imation in a suitable functional form and by performing a seriesof steady state fundamental harmonic voltage measurements inthe field weakening region, it becomes possible to determinethe correct magnetizing curve approximation purely by visualinspection of the measurement results [34]. An experimentalillustration of this method is given in Fig. 3, where measuredline-to-line fundamental voltage component is shown, togetherwith the reconstructed magnetizing curve.

Another magnetizing curve identification procedure isdescribed in [35]. It relies on the signals that are alreadypresent within the drive controller (stator currents and the dclink voltage), so that additional measurements are not required.

TOLIYAT et al.: A REVIEW OF RFO INDUCTION MOTOR PARAMETER ESTIMATION TECHNIQUES 273

Fig. 2. An experimental trial-and-error method of rotor time constant tuningin indirect vector controller: speed response to alternating square-wave torquecommand with correct rotor time constant and with 1.7 times correct rotor timeconstant (0.75-kW machine). Speed response is a triangular function of timewhen the rotor time constant is correctly set (upper figure). The method wasoriginally proposed in [31] and the results shown are from [4].

A special identification function, proposed in [35], ensuresprecise acquisition of the magnetizing curve, robust againstthe stator resistance variation, and the inverter lock-out time.The algorithm does not require any test signals. It is sufficientto perform the measurements during running of the unloadedmotor at around 100 r/min. Performing measurements at such alow speed enables the impact of iron and mechanical losses onidentification accuracy to be minimized. This, in turn, enablesaccurate identification down to 10% of the rated magnetizingflux, including the point of infliction. An illustration of theresults of the procedure of [35] is given in Fig. 4.

Some other approaches to the magnetizing curve identifica-tion, described in [36][38] are more involved and therefore lesssuitable for onsite commissioning of the drive. Method of [36]performs identification at standstill and only current measure-ments are needed. However, all the three phases of the machineare energized and standstill condition is achieved by means ofclosed loop speed control. The method requires that the vectorcontrolled induction motor is coupled to a controllable load andis therefore not suitable for onsite commissioning. Similar con-clusion applies to the broad-band excitation method [37], whichrequires injection of multiple frequency supply into the ma-chines stator terminals. Method of [38], although apparentlyvery accurate, is rarely applicable in practice since it requiresthat the neutral point of the stator star connected winding isaccessible.

Fig. 3. Measured fundamental stator voltage for different settings of theparameter a of the inverse magnetizing curve per-unit analytical approximationi = a + (1 a) and reconstructed magnetizing curve(2.3-kW machine, field-weakening starts at 1150 r/min; results taken from[34]). The correct value is a = 0:9 since it gives the flattest voltage behaviorin the field-weakening region.

Fig. 4. Experimentally identified magnetizing curve and magnetizinginductance (100 r/min, no-load conditions, 7-kW machine, method of [35]).

It is worth noting that offline magnetizing curve (or magne-tizing inductance) identification suffices for saturation compen-sation schemes and that identification of dynamic (differential)inductance is usually not required. However, there are methodsthat enable identification of the dynamic inductance as well, forexample [37] and [39].

Compensation of iron losses in vector controlled inductionmachines usually requires knowledge of the equivalent iron loss

274 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 2, JUNE 2003

resistance for the fundamental harmonic, which is a function ofthe fundamental frequency [8]. The equivalent iron loss resis-tance can be identified during the drive commissioning usingthe procedure outlined in [40]. A series of no-load tests areperformed at various fundamental frequencies, using the samePWM voltage source inverter that will be subsequently used forthe normal drive operation. Fundamental harmonic input powerneeds to be measured, mechanical losses are separated fromthe fundamental iron losses using the customary no-load testprocedure, and equivalent iron loss resistance is eventually cal-culated. The procedure requires that rotation is permitted andthat no-load condition is available. An illustration of the ex-perimental results related to fundamental iron loss componentand the corresponding equivalent iron loss resistance is givenin Fig. 5. Tests at standstill, which would enable identificationof the equivalent iron loss resistance, do not seem to exist atpresent.

It should be noted that accuracy of parameter determination inall offline identification techniques depends on the sample rateselection, quantization errors, resolution and accuracy of sen-sors, etc. [41]. Identified parameter values will therefore alwaysbe characterized with certain error margin. The major problemencountered in offline parameter identification at standstill isundoubtedly the inverter lock-out time and nonlinearity, whichmake the accurate parameter determination on the basis of re-constructed voltages very difficult without prior knowledge ofthe inverter voltage drop characteristics [42]. A technique forovercoming this problem has recently been proposed, based onrecursive least squares [43].

Further important works describing various approaches toself-commissioning and commissioning are those of [44][55].

IV. ONLINE ROTOR TIME CONSTANT ESTIMATION TECHNIQUES

The major effort has been put into development of rotor timeconstant (rotor resistance) online estimation methods. Due toa huge number of proposed solutions of very different nature,these are further classified into four subgroups.

A. Spectral Analysis TechniquesThis group of methods encompasses all of the cases where

online identification is based on the measured response to a de-liberately injected test signal or an existing characteristic har-monic in the voltage/current spectrum [56][66]. Stator currentsand/or voltages of the motor are sampled and the parameters arederived from the spectral analysis of these samples. In the caseof spectral analysis, a perturbation signal is used because underno-load conditions of the induction motor, the rotor induced cur-rents and voltages become zero, so slip frequency becomes zero,and hence, the rotor parameters cannot be estimated. In [56] and[57], the disturbance to the system is provided by injecting nega-tive sequence components. An online technique for determiningvalue of the rotor resistance by detecting the negative sequencevoltage is proposed in [56]. Special precautions need to be takento circumvent the torque-producing action when an inductionmotor, equipped with this system, is used as a torque drive; oth-erwise, the outer loop might prevent the perturbation from beinginjected into the system. The main drawback of this method is

Fig. 5. Fundamental component of the iron loss identified using the procedureof [40] and the corresponding equivalent iron loss resistance (4-kW machine).

Fig. 6. Rotor inductance and rotor resistance identification using the methodof [57] (simulation results).

that the strong second harmonic torque pulsation is induced dueto the interaction of positive and negative rotating componentsof MMF.

In [57], an online estimation technique is proposed, based onthe model in the frequency domain. The -axis componentof the injected negative sequence component is kept at zero, sothat the machine torque is undisturbed. The -axis componentaffects the flux of the machine. FFT is used to analyze thecurrents and voltages and the fundamental components of thesampled spectral values are used to determine the parameters.Average speed is used for the identification of parameters.The simulation results, obtained using this method, for rotorresistance and rotor inductance identification are given in Fig. 6[57].

TOLIYAT et al.: A REVIEW OF RFO INDUCTION MOTOR PARAMETER ESTIMATION TECHNIQUES 275

In [58], an attempt to create online tests similar to the no-loadand full-load tests is made. In [59], a pseudo-random binary se-quence signal is used for perturbation of the system by injectingit into the -axis and correlating with -axis stator current re-sponse. The sign of correlation gives the direction for rotor timeconstant updating. This method however does not work satisfac-torily under light loads. In [60], a sinusoidal perturbation is in-jected into the flux producing stator current component channel.Though rotor resistance can be estimated under any load andspeed condition, the cost is high due to the installation of twoflux search coils.

Solutions described in [61][63] all belong to the samecategory. A very different approach is the one described in[64][66], where rotor slot harmonics in stator current aretracked and used for online updating of the rotor time constant.

B. Observer-Based TechniquesIn [67], Loron and Lalibert describe the motor model and

the development and tuning of an extended Kalman filter (EKF)for parameter estimation during normal operating conditionswithout introducing any test signals. The proposed method re-quires terminal and rotor speed measurements and is useful forautotuning an indirect field-oriented controller or an adaptivedirect field-oriented controller. In [68], Zai, DeMarco, and Lipopropose a method for detection of the inverse rotor time con-stant using the EKF by treating the rotor time constant as thefifth state variable along with the stator and rotor currents. Thisis similar to a previously mentioned method that injected per-turbation in the system, except that in this case, the perturbationis not provided externally. Instead, the wide-band harmonicscontained in a PWM inverter output voltage serve as an excita-tion. This method works on the assumption that when the motorspeed changes, the machine model becomes a two-input/two-output time-varying system with superimposed noise input. Thedrawbacks are that this method assumes that all other parame-ters are known and the variation in the magnetizing inductancecan introduce large errors into the rotor time constant estima-tion. The application of the EKF for slip calculation for tuningan indirect field oriented drive is proposed in [69]. Using theproperty that in the steady state the Kalman gains are asymptot-ically constant for constant speeds, the Riccati difference equa-tion is replaced by a look-up table that makes the system muchsimpler. The disadvantage is that, although the complexity ofthe Riccati equation is reduced, the full-order EKF is computa-tionally very intensive as compared to the reduced order-basedsystems.

In [70], an online estimation of rotor resistance and the mag-netizing inductance, using continuous form of the Kalman filteris proposed, though the actual estimation is done offline usingthe discrete form of the KF. For using the KF online, it is im-portant to estimate the magnetizing inductance accurately as aninaccurate magnetizing inductance gives improper value of therotor time constant. The method is based on the assumption thatsince the value of the magnetizing inductance follows the motorflux level, the magnetizing inductance can be estimated alongwith the rotor resistance and the rotor time constant using theKF. Other solutions, based on the Kalman filter, are those de-scribed in [71][76].

An extended Luenberger observer (ELO) for joint state andparameter estimation was developed in [77][79]. In [78] and[79], the authors have provided a comparison of the operationof the ELO and the EKF. In [78], a deterministic approach todesigning the ELO with joint online estimation of motor statesand parameters is presented. In [79], Du and Brdys implementedthe scheme using three different full-order ELOs. The first ELOwas used for rotor time constant and rotor flux estimation. Thesecond one was used for shaft speed and rotor flux estima-tion and the third for shaft speed, load torque, and rotor fluxestimation.

In the case of joint state and parameter estimation, ELO turnsout to be the advantageous solution. Since the induction motor isa nonlinear system, the observations from the EKF at individualtime instants do not lead to an overall optimal observation. Forthe ELO, there is a great deal of flexibility in choosing the gain,unlike the EKF and the rate of convergence can be tuned withoutadversely affecting the steady state accuracy of the observer.The main advantage of the ELO over the EKF is that the ob-server performance can be greatly enhanced by simply adjustingthe gain matrix for rapid convergence of the estimates, whichgives an unbiased estimation in the case of the ELO.

The major problems related to EKF and ELO applications arecomputational intensity and the fact that all the inductances aretreated as constants in the motor equations. In order to improvethe accuracy of the EKF-based rotor resistance identification, itis suggested in [68], [70], and [73] to simultaneously identifythe magnetizing inductance. Another possibility of improvingthe accuracy is the inclusion of the iron loss into the model [72].

C. Model Reference Adaptive System-Based TechniquesThe third major group of online rotor resistance adaptation

methods is based on principles of model reference adaptivecontrol. This is the approach that has attracted most of theattention due to its relatively simple implementation require-ments. The basic idea is that one quantity can be calculated intwo different ways. The first value is calculated from referencesinside the control system. The second value is calculated frommeasured signals. One of the two values is independent of therotor resistance (rotor time constant). The difference betweenthe two is an error signal, whose existence is assigned entirelyto the error in rotor resistance used in the control system. Theerror signal is used to drive an adaptive mechanism (PI or Icontroller) which provides correction of the rotor resistance.Any method that belongs to this group is based on utilizationof the machines model and its accuracy is therefore heavilydependent on the accuracy of the applied model. The numberof methods that belong to this group is vast [80][100] andthey primarily differ with respect to which quantity is selectedfor adaptation purposes. Reactive power-based method is notdependent on stator resistance at all and is probably the mostfrequently applied approach [80][85]. A method based onspecial criterion function, derived again from stator voltageand current measurement, is described in [86]. Next, air gappower can be selected as the quantity on which adaptationis based [87], [88]. The reference air gap power is calcu-lated from reference torque and frequency values, while theactual one has to be calculated from measured input power

276 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 2, JUNE 2003

and estimated stator losses in the machine. Alternatively, dclink power can be measured instead of the machines inputpower. In both cases, the accuracy of the method is heavilyundermined by the need to estimate stator loss (and inverterlosses if dc link power is measured). Other possibilities includeselection of torque [83], [89], rotor back emf [90], [91], rotorflux magnitude [83], rotor flux -, and -components [92],stored magnetic energy [93], product of stator -axis currentand rotor flux [94], stator fundamental rms voltage [95], stator

-axis or -axis voltage components [94], or stator -axis cur-rent component [96]. There are a couple of common featuresthat all of the methods of this group share. First, rotor resis-tance adaptation is usually operational in steady-states onlyand is then disabled during transients. Thus, the adaptationcan be based on steady-state model of the machine. Second,in the vast majority of cases, stator voltages are required forcalculation of the adaptive quantity and they have either to bemeasured or reconstructed from the inverter firing signals andmeasured dc link voltage. Third, in most cases, identificationdoes not work at zero speed and at zero load torque. Finally,identification heavily relies on the model of the machine, inwhich, most frequently, all of the other parameters are treatedas constants. This is at the same time the major drawback ofthis group of methods. Indeed, an analysis of the parametervariation influence on accuracy of rotor resistance adaptation[101] shows that when rotor flux magnitude method is appliedand actual leakage inductances deviate by 40% from the valuesused in the adaptation, rotor resistance is estimated with suchan error that the response of the drive becomes worse than withno adaptation at all. Similar study, with very much the sameconclusions, is described in [102] where parameter sensitivityis examined for -axis stator voltage method, -axis statorvoltage method, air gap power method, and reactive powermethod.

Due to high sensitivity of the model-based methods to otherparameter variation effects, it is desirable to account for at leastsome of these in the process of rotor resistance adaptation.Variation of the magnetizing inductance with saturation is forthis reason sometimes taken into account, so that the accu-racy of rotor resistance identification is improved [84], [86],[103], [104]. The other drawback of this group of methods,impossibility of adaptation at zero speed and zero load torque,is successfully eliminated in certain cases. For example, theschemes of [86] and [96] are operational at zero speed and atlight loads although they do fail at zero load.

Operation of a MRAS rotor resistance adaptation scheme isillustrated in Fig. 7 by means of experimentally recorded traces.The method based on special criterion function of [86], whichenables rotor resistance adaptation at zero speed and under lightloading conditions, is implemented. The error function, whichserves as the input into the PI controller, is shown together withthe rotor resistance estimate in per unit (i.e., ratio of rotor re-sistance in the controller to the actual one in the machine). Thedrive operates at zero speed with 0.2 per unit load torque. Theadaptation mechanism operation is illustrated for step variationof rotor resistance used in the controller, of 50 . As can beseen from Fig. 7, rotor resistance adaptation works well as theresistance in the controller always returns, after the introduceddisturbances, to the previous value (i.e., to ).

Fig. 7. Experimental recording of the operation of the rotor resistanceadaptation in indirect rotor flux oriented induction machine, using the methodof [86] (scales: time10 s/div, error function0.5 p.u./div, rotor resistanceestimate0.4 p.u./div; 0.75-kW machine). Figure provided courtesy of theauthor, Dr. S.N. Vukosavic.

Other methods of online rotor resistance adaptation, that donot belong to any of the three main groups, are reviewed next.

D. Other MethodsThere exist a number of other possibilities for online rotor

resistance (rotor time constant) adaptation, such as those de-scribed in [105][107]. For example, the method of [107] doesnot require either a special test signal or complex computations.It is based on a special switching technique of the current regu-lated PWM inverter, which allows measurement of the inducedvoltage across the disconnected stator phase. The rotor time con-stant is then identified directly from this measured voltage andmeasured stator currents. The technique provides up to six win-dows within one electric cycle to update the rotor time constant,which is sufficient for all practical purposes. A simulation il-lustration of the method is given in Fig. 8, where estimated andactual rotor time constant are shown. The updating is performedonly twice (rather than six times) during one electrical cycle.

Another possibility, opened up by the recent developmentsin the area of artificial intelligence (AI), is the application ofartificial neural networks for the online rotor time constant(rotor resistance) adaptation. Such a possibility is exploredin [108][112]. The other AI technique that can be utilizedfor online rotor time constant adaptation is the fuzzy logic[113][120].

Recent emphasis on sensorless vector control has led to a de-velopment of a number of schemes for simultaneous rotor speedand rotor time constant online estimation, that are applicable inconjunction with the appropriate speed estimation model-basedalgorithms [121][134]. These methods of rotor time constantestimation belong in vast majority of cases to one of the groupsalready reviewed in this section.

An excellent review of the rotor resistance compensationschemes, available at the time, is the one of [135].

V. ONLINE ESTIMATION OF STATOR RESISTANCE

An industrially accepted standard for sensored rotor flux ori-ented control has become the indirect rotor flux oriented control(IRFOC), which does not require the knowledge of the stator re-sistance. Since the rotor time constant is of crucial importance

TOLIYAT et al.: A REVIEW OF RFO INDUCTION MOTOR PARAMETER ESTIMATION TECHNIQUES 277

Fig. 8. Estimated and actual rotor time constant using the procedure of[107]. The estimate is updated twice per electrical cycle, on the basis of themeasurement of the voltage across a disconnected phase.

for decoupled flux and torque control in IRFOC, the major ef-fort was directed toward development of online techniques forrotor time constant identification, as shown by the review in Sec-tion IV. The situation has however dramatically changed withthe advent of sensorless vector control, which requires rotorspeed estimation. Vast majority of speed estimation techniquesare based on the induction machine model and involve the statorresistance as a parameter in the process of speed estimation. Anaccurate value of the stator resistance is of utmost importancein this case for correct operation of the speed estimator in thelow speed region. If stator resistance is detuned, large speed es-timation errors and even instability at very low speeds result. Itis for this reason that online estimation of stator resistance hasreceived considerable attention during the last decade, as wit-nessed by a large number of publications devoted to this subject[136][163]. The other driving force behind the increased in-terest in online stator resistance estimation was the introductionof direct torque control (DTC), which in its basic form relieson estimation of stator flux from measured stator voltages andcurrents. The accuracy of DTC, especially in the low frequencyregion, therefore heavily depends on the knowledge of the cor-rect stator resistance value.

In general, methods of stator resistance estimation are sim-ilar to those utilized for rotor time constant (rotor resistance)estimation and include application of observers, extendedKalman filters, model reference adaptive systems, and artificialintelligence.

VI. ONLINE COMPENSATION OF SATURATION AND IRON LOSS

In contrast to temperature-related resistance variation that isslow, change in machines inductances is very rapid. Compen-sation of such variations is therefore most easily accomplishedby means of modified nonlinear machine models that accountfor the variable degree of saturation and invariably ask for theknowledge of an appropriate magnetizing curve. Compensationof main flux saturation, that will simultaneously yield onlinemagnetizing inductance estimation, requires that the basic ma-chine model is modified in such a way that the nonlinearity ofthe magnetizing curve is accounted for. The standard assump-tion is that leakage flux and main flux components of the statorand rotor flux can be treated independently. It is assumed fur-ther on that leakage inductances are constants and that only mainflux saturates.

Derivation of the complete dynamic axis models thataccount for main flux saturation is rather involved and thefinal form depends on the selected set of state space variables[164][166]. However, if one is interested only in modifyingthe rotor flux estimators or the indirect vector controller in sucha way that the main flux saturation is compensated, then thistask can be accomplished in a relatively simple way, becauseall of the estimators and the indirect vector controller are basedon the reduced order models of an induction machine [1],[167][170]. Very much the same applies to the utilization of afull order observer for rotor flux estimation, provided that theobserver is constructed using stator current and rotor flux axis components as state space variables [171]. In all of thesecases, knowledge of the induction machines magnetizing curveis a prerequisite, since this characteristic has to be incorporatedinto the control system. Magnetizing curve has therefore to beidentified offline during the commissioning of the drive.

The other existing approaches to online magnetizing induc-tance estimation are predominantly based on standard axismachine model and they do not require a-priori knowledge ofthe magnetizing curve. Such is the situation with methods re-ported in [103], [172][178]. While the estimation is sufficientlygood in steady state, it is usually of limited accuracy during tran-sients, since the schemes are based on the induction machinemodel that accounts for the main flux saturation in a very ap-proximate way (only through continuous variation of the steadystate magnetizing inductance). A couple of theoretical/ simula-tion attempts were made recently to apply AI techniques (ANNsand FL) in the estimation of the saturated magnetizing induc-tance [179], [180].

Online magnetizing inductance estimation is illustrated inFig. 9 for a model-based method, described in [181], whichrequires knowledge of the magnetizing curve of the machine.An experimental recording of the start-up transient, withset speed of 1350 r/min, is shown. The machine is initiallypremagnetized and the field weakening operation starts at650 r/min by means of the IRFOC scheme described in [34].The magnetizing inductance exhibits substantial variation, fromunsaturated value in premagnetized state to rated saturatedvalue and then back toward unsaturated value as the speed ofrotation in the field weakening region increases.

Rotor leakage flux saturation can be included in the model ofthe machine by making rotor leakage inductance a variable pa-rameter, dependent on the rotor current. Frequency-related vari-ation of rotor parameters can be accounted for by representingthe rotor winding with two branches. A scheme with air gapflux oriented control, that includes both compensation of rotorleakage flux saturation and frequency dependent variation ofrotor parameters, derived from a modified induction machinemodel that accounts for both of these phenomena, is described indetail in [182][184]. It is demonstrated in [182][184] that, forthe chosen machine in which both of these effects are severelypronounced, vector control scheme derived from such a mod-ified model provides superior performance when compared tothe performance obtainable with vector control scheme based onthe constant parameter model. It is worth noting that the schemeof [182][184] additionally compensates for main flux satura-tion as well. The intrinsic difficulty in implementation of sucha vector control scheme is that the number of rotor parameters

278 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 2, JUNE 2003

Fig. 9. Online estimated magnetizing inductance and speed of rotation forthe accleration transient of a loaded machine, using the method of [181]. Fieldweakening starts at 650 r/min.

that have to be determined during the commissioning is now fiverather than two. One possibility is to use finite element calcu-lations, as suggested in [182]. Alternatively, a series of lockedrotor tests, executed for different current values at various oper-ating frequencies, can be used to experimentally identify offlinethe parameters of this modified model.

Compensation of iron loss is nowadays almost exclusivelydone using the model-based approach, which consists of de-velopment of a modified vector control scheme on the basisof a machines model that takes into account the existence ofthe iron loss. Iron loss is represented within the machine modelwith either a parallel or a series equivalent iron loss resistanceand a modified vector control strategy is then derived. This ap-proach requires equivalent iron loss resistance offline identifica-tion at the commissioning stage. The examples of utilization ofthis compensating strategy are numerous and include [8], [40],[185][205].

A very different approach to equivalent iron loss identifica-tion and adaptive iron loss compensation is described in [206],[207]. It is based on the fact that an error in the rotor flux po-sition estimate is inevitably introduced by the existence of theiron loss [8]. This error can be brought down to zero only ifthe iron loss-compensating signal relies on the correct value ofthe equivalent iron loss resistance for the given operating condi-tions. An online tuning scheme is hence developed, which pro-vides quasi steady state tuning of the equivalent iron loss resistoron the basis of the stator -axis voltage error signal. The methodrequires stator voltage and current measurement but avoids theneed for offline equivalent iron loss resistance identification.

VII. CONCLUSIONHigh performance control schemes of an induction motor in-

variably rely on the knowledge of at least some of the motor pa-

rameters. Parameter values are used within the drive controllerand they have therefore to be identified offline, during the drivecommissioning. However, since all of the parameters inevitablyvary during the drive operation, it is often desirable to improvethe performance of the drive by adding an online parameter esti-mator. Such a situation has led to development of a large numberof offline parameter identification and online parameter estima-tion methods during the last two decades. An attempt is made inthis paper to review the existing methods and to provide a com-prehensive bibliography on the subject.

The attention is at first focused on self-commissioning andcommissioning techniques that serve the purpose of the offlineparameter identification at the stage of the drive initialization.Available methods for induction motor equivalent circuit param-eter identification are reviewed, along with the possibilities forthe magnetizing curve and equivalent iron loss resistance deter-mination. Since an accurate value of the rotor time constant isof utmost importance for tuned operation of the vast majorityof vector controlled induction motor drives, a substantial spaceis further devoted to the methods that enable online rotor timeconstant estimation. This is followed by discussion of the onlinestator resistance estimation methods, since the exact knowledgeof the stator resistance is of paramount importance in a numberof sensorless vector and direct torque control schemes.

In contrast to the resistance variations that are slow, varia-tions in the magnetizing inductance and iron loss are rapid andare therefore most easily compensated by utilizing a modifiedvector controller, that is developed using an appropriately mod-ified motor model (that accounts for the flux saturation and/oriron loss) as the starting point. Methods aimed at online estima-tion and compensation of the magnetizing inductance variationand the iron loss are surveyed in the last section of the paper.

The paper is illustrated throughout with numerous experi-mental and simulation results, related to different offline param-eter identification and online parameter estimation techniques,taken from various publications of the authors.

REFERENCES[1] D. W. Novotny and T. A. Lipo, Vector Control and Dynamics of AC

Drives. Oxford: Clarendon Press, 1996.[2] R. Krishnan, Electric Motor Drives: Modeling, Analysis and Con-

trol. Englewood Cliffs, NJ: Prentice-Hall, 2001.[3] M. P. Kazmierkowski and H. Tunia, Automatic Control of Converter-Fed

Drives. Amsterdam, The Netherlands: Elsevier, 1994.[4] E. Levi, Magnetic variables control, in Wiley Encyclopedia of

Electrical and Electronics Engineering, J. G. Webster, Ed. New York:Wiley, 1999, vol. 12, pp. 242260.

[5] P. Vas, Sensorless Vector and Direct Torque Control. London, U.K.:Oxford Univ. Press, 1998.

[6] , Parameter Estimation, Condition Monitoring, and Diagnosis ofElectrical Machines. Oxford: Clarendon Press, 1993.

[7] R. D. Lorenz, T. A. Lipo, and D. W. Novotny, Motion control withinduction motors, in Power Electronics and Variable FrequencyDrivesTechnology and Applications, B. K. Bose, Ed. New York,NY: IEEE Press, 1997, pp. 209276.

[8] E. Levi, Impact of iron loss on behavior of vector controlled induc-tion machines, IEEE Trans. Ind. Applicat., vol. 31, pp. 12871296,Nov./Dec. 1995.

[9] H. Schierling, Self-commissioningA novel feature of moderninverter-fed induction motor drives, in Proc. Inst. Elect. Eng. Conf.Power Electron. Variables Speed Drives, 1988, pp. 287290.

[10] R. Jtten and E. D. Lettner, Adaptive and self-commissioning controlfor a drive with induction motor and current source inverter, in Proc.Europe. Conf. Power Electron. Applicat., 1989, pp. 627632.

TOLIYAT et al.: A REVIEW OF RFO INDUCTION MOTOR PARAMETER ESTIMATION TECHNIQUES 279

[11] M. Sumner and G. Asher, Self-commissioning for voltage-referencedvoltage fed vector controlled induction motor drives, in Proc. IEEEPower Electron. Spec. Conf., 1992, pp. 139144.

[12] , Autocommissioning for voltage-referenced voltage-fed vectorcontrolled induction motor drives, Proc. Inst. Elect. Eng., pt. B, vol.140, no. 3, pp. 187200, 1993.

[13] M. Ruff, A. Bnte, and H. Grotstollen, A new self-commissioningscheme for an asynchronous motor drive system, in Proc. IEEE Ind.Applicat. Soc. Annu. Meeting, 1994, pp. 612623.

[14] A. M. Khambadkone and J. Holtz, Vector-controlled induction motordrive with a self-commissioning scheme, IEEE Trans. Ind. Electron.,vol. 38, pp. 322327, Oct. 1991.

[15] W. H. Kwon, C. H. Lee, K. S. Youn, and G. H. Cho, Measurement ofrotor time constant taking into account magnetizing flux in the induc-tion motor, in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1994, pp.8892.

[16] M. Globevnik, Induction motor parameters measurement at standstill,in Proc. IEEE Ind. Electron. Soc. Annu. Meeting, 1998, pp. 280285.

[17] P. J. Chrzan and H. Klaassen, Parameter identification of vector-con-trolled induction machines, Electrical Engineering, vol. 79, no. 1, pp.3946, 1996.

[18] S. I. Moon and A. Keyhani, Estimation of induction machine parame-ters from standstill time-domain data, IEEE Trans. Ind. Applicat., vol.30, pp. 16061615, Nov./Dec. 1994.

[19] M. Ruff and H. Grotstollen, Identification of the saturated mutualinductance of an asynchronous motor at standstill by recursive leastsquares algorithm, in Proc. Europe. Conf. Power Electron. Applicat.,vol. 5, 1993, pp. 103108.

[20] A. Consoli, L. Fortuna, and A. Gallo, Induction motor identificationby a microcomputer-based structure, IEEE Trans. Ind. Electron., vol.IE-34, pp. 422428, Nov. 1987.

[21] A. Bnte and H. Grotstollen, Parameter identification of an inverter-fedinduction motor at standstill with a correlation method, in Proc. Europe.Conf. Power Electron. Applicat., vol. 5, 1993, pp. 97102.

[22] N. R. Klaes, Parameters identification of an induction machine withregard to dependencies on saturation, in Proc. IEEE Ind. Applicat. Soc.Annu. Meeting, 1991, pp. 2127.

[23] A. Bnte and H. Grotstollen, Offline parameter identification of an in-verter-fed induction motor at standstill, in Proc. Europe. Conf. PowerElectron. Applicat., 1995, pp. 3.4923.496.

[24] R. J. Kerkman, J. D. Thunes, T. M. Rowan, and D. Schlegel, A fre-quency based determination of the transient inductance and rotor resis-tance for field commissioning purposes, in Proc. IEEE Ind. Applicat.Soc. Annu. Meeting, 1995, pp. 359366.

[25] C. Wang, D. W. Novotny, and T. A. Lipo, An automated rotor timeconstant measurement system for indirect field-oriented drives, IEEETrans. Ind. Applicat., vol. 24, pp. 151159, Jan./Feb. 1988.

[26] M. Bertoluzzo, G. S. Buja, and R. Menis, Self-commissioning of RFOIM drives: One-test identification of the magnetization characteristicof the motor, IEEE Trans. Ind. Applicat., vol. 37, pp. 18011806,Nov./Dec. 2001.

[27] M. Ruff and H. Grotstollen, Off-line identification of the electrical pa-rameters of an industrial servo drive system, in Proc. IEEE Ind. Ap-plicat. Soc. Annu. Meeting, 1996, pp. 213220.

[28] H. Rasmussen, M. Knudsen, and M. Tonnes, Parameter estimation ofinverter and motor model at standstill using measured currents only, inProc. IEEE Int. Symp. Ind. Electron., 1996, pp. 331336.

[29] M. Bertoluzzo, G. Buja, and R. Menis, Identification techniques of in-duction motor parameters for self-commissioning field-oriented drives,Automatika, vol. 38, no. 34, pp. 103115, 1997.

[30] G. Buja and R. Menis, Identification of the magnetizing characteristicof an induction motor incorporated into a field-oriented drive for self-commissioning purposes, in Proc. Int. Conf. Power Electron. MotionContr., 1998, pp. 3.753.80.

[31] R. D. Lorenz, Tuning of field oriented induction motor controllers forhigh performance applications, in Proc. IEEE Ind. Applicat. Soc. Annu.Meeting, 1985, pp. 607612.

[32] A. Boglietti, P. Ferraris, M. Lazzari, and F. Profumo, Induction motorequivalent circuit parameters determination from standard tests madewith inverter supply, in Proc. Inst. Elect. Eng. Elect. Mach. DrivesConf., 1993, pp. 271276.

[33] Y. N. Lin and C. L. Chen, Automatic induction motor parameter mea-surement under sensorless field-oriented control, in Proc. IEEE Int.Symp. Ind. Electron., 1996, pp. 894899.

[34] E. Levi, M. Sokola, and S. N. Vukosavic, A method for magnetizingcurve identification in rotor flux oriented induction machines, IEEETrans. Energy Conversion, vol. 15, pp. 157162, June 2000.

[35] E. Levi and S. N. Vukosavic, Identification of the magnetizing curveduring commissioning of a rotor flux oriented induction machine, Proc.Inst. Elect. Eng.Elect. Power Applicat., vol. 146, no. 6, pp. 685693,1999.

[36] E. Levi, Method of magnetizing curve identification in vector con-trolled induction machines, Europe. Trans. Elect. Power, vol. 2, no. 5,pp. 309314, 1992.

[37] A. Ganji, P. Guillaume, R. Pintelon, and P. Lataire, Induction motor dy-namic and static inductance identification using a broadband excitationtechnique, IEEE Trans. Energy Conversion, vol. 13, pp. 1520, March1998.

[38] A. Stankovic, E. R. Benedict, V. John, and T. A. Lipo, A novel methodfor measuring induction machine magnetizing inductance, in Proc.IEEE Ind. Applicat. Soc. Annu. Meeting, 1997, pp. 234238.

[39] V. Staudt, Measuring differential inductances of asynchronous ma-chines, Europe. Trans. Elect. Power, vol. 4, no. 1, pp. 2733, 1994.

[40] E. Levi, M. Sokola, A. Boglietti, and M. Pastorelli, Iron loss in rotorflux oriented induction machine: Identification, assessment of detuningand compensation, IEEE Trans. Power Electron., vol. 11, pp. 698709,Sept. 1996.

[41] D. E. Borgard, G. Olsson, and R. D. Lorenz, Accuracy issues for param-eter estimation of field oriented induction machine drives, IEEE Trans.Ind. Applicat., vol. 31, pp. 795801, July/Aug. 1995.

[42] G. S. Buja, R. Menis, and M. I. Valla, MRAS identification of the in-duction motor parameters in PWM inverter drives at standstill, in Proc.IEEE Ind. Electron. Soc. Annu. Meeting, 1995, pp. 1041-1041-.

[43] M. Bertoluzzo, G. S. Buja, and R. Menis, Inverter voltage drop-freerecursive least-squares parameter identification of a PWM inverter-fedinduction motor at standstill, in Proc. IEEE Int. Symp. Ind. Electron.,1997, pp. 649654.

[44] A. Gastli, Identification of induction motor equivalent circuit parame-ters using the single-phase test, IEEE Trans. Energy Conversion, vol.14, pp. 5156, Mar. 1999.

[45] T. Kudor, K. Ishihara, and H. Naitoh, Self-commissioning for vectorcontrolled induction motors, in Power Electronics Technology and Ap-plications II, F. C. Lee, Ed. New York, NY: IEEE Press, 1997, pp.509516.

[46] A. Bellini, G. Franceschini, C. Tassoni, and F. Filippetti, A self-com-missioning scheme for field-oriented induction motor drives, in Proc.Europe. Conf. Power Electron. Applicat., 1999, [CDROM] Paper no.379.

[47] H. S. Choi and S. K. Sul, Automatic commissioning for vector con-trolled ac motors using Walsh functions, in Proc. IEEE Ind. Applicat.Soc. Annu. Meeting, 1999, pp. 12841289.

[48] J. Godbersen, A stand-still method for estimating the rotor resistanceof induction motors, in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting,1999, pp. 900905.

[49] C. B. Jacobina, J. E. Chaves, and A. M. N. Lima, Estimating the pa-rameters of induction machines at standstill, in Proc. IEEE Int. Elect.Mach. Drives Conf., 1999, pp. 380382.

[50] F. Barrero, J. Perez, R. Millan, and L. G. Franquelo, Self-commis-sioning for voltage-referenced voltage-fed vector controlled inductionmotor drives, in Proc. IEEE Ind. Electron. Soc. Annu. Meeting, 1999,pp. 10331038.

[51] H. B. Karayaka and M. N. Marwali, Induction machine parametertracking from test data via PWM inverters, in Proc. IEEE Ind. Applicat.Soc. Annu. Meeting, 1997, pp. 227233.

[52] L. A. de S. Ribeiro, C. B. Jacobina, A. M. N. Lima, and A. C. Oliviera,Real-time estimation of the electric parameters of an induction machineusing sinusoidal PWM voltage waveforms, IEEE Trans. Ind. Applicat.,vol. 36, pp. 743754, May/June 2000.

[53] Z. M. A. Peixoto and P. F. Seixas, Electrical parameter estimation con-sidering the saturation effects in induction machines, in Proc. IEEEPower Elect. Spec. Conf., 2000, pp. 15631568.

[54] , Identification at standstill of induction machines including thesaturation effects, in Proc. Power Electron. Motion Contr. Conf., 2000,pp. 6.116.16.

[55] J. K. Seok and S. K. Sul, Induction motor parameter tuning for high-performance drives, IEEE Trans. Ind. Applicat., vol. 37, pp. 3541,Jan./Feb. 2001.

[56] T. Matsuo and T. A. Lipo, A rotor parameter identification scheme forvector controlled induction motor drives, IEEE Trans. Ind. Applicat.,vol. IA-21, pp. 624632, May/June 1985.

[57] H. A. Toliyat and A. A. GH. Hosseiny, Parameter estimation algorithmusing spectral analysis for vector controlled induction motor drives, inProc. IEEE Int. Symp. Ind. Electron., 1993, pp. 9095.

280 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 2, JUNE 2003

[58] H. Chai and P. P. Acarnley, Induction motor parameter estimation al-gorithm using spectral analysis, Proc. Inst. Elect. Eng., pt. B, vol. 139,no. 3, pp. 165174, 1992.

[59] R. Gabriel and W. Leonhard, Microprocessor control of inductionmotor, in Proc. Int. Semiconductor Power Conversion Conf., 1982, pp.385396.

[60] H. Sugimoto and S. Tamai, Secondary resistance identification of aninduction motorApplied model reference adaptive system and itscharacteristics, IEEE Trans. Ind. Applicat., vol. IA-23, pp. 296303,Mar./Apr. 1987.

[61] T. Saitoh, K. Okuyama, and T. Matsui, An automated secondary resis-tance identification scheme in vector controlled induction motor drives,in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1989, pp. 594600.

[62] E. Cerruto, A. Consoli, A. Raciti, and A. Testa, Slip gain tuning in in-direct field oriented control drives, Electric Machines and Power Sys-tems, vol. 23, pp. 6379, 1995.

[63] K. Tungpimolrut, F. Z. Peng, and T. Fukao, A robust rotor time con-stant estimation method for vector control of induction motor under anyoperating condition, in Proc. IEEE Ind. Electron. Soc. Annu. Meeting,1994, pp. 275280.

[64] J. Cilia, G. M. Asher, J. Shuli, M. Sumner, K. J. Bradley, and A. Ferrah,The recursive maximum likelihood algorithm for tuning the rotor timeconstant in high-performance sensorless induction motor drives, inProc. Int. Conf. Elect. Mach., 1998, pp. 926930.

[65] R. Beguenane and G. A. Capolino, Induction motor rotor time con-stant measurement for vector control drives without rotary transducer,in Proc. IEEE Int. Symp. Power Eng. Power Technol., vol. 3, 1995, pp.1317.

[66] R. Beguenane, C. Ghyselen, and H. Schoorens, A proposed inductionmotor speed sensor without contact from slot harmonics. Applicationto rotoric time constant identification, in Proc. Inst. Elect. Eng. Conf.Power Electron. Variable-Speed Drives, 1994, pp. 9095.

[67] L. Loron and G. Lalibert, Application of the extended Kalman filterto parameters estimation of induction motors, in Proc. Europe. Conf.Power Electron. Applicat., vol. 5, 1993, pp. 8590.

[68] L. C. Zai, C. L. DeMarco, and T. A. Lipo, An extended Kalman filterapproach to rotor time constant measurement in PWM induction motordrives, IEEE Trans. Ind. Applicat., vol. 28, pp. 96104, Jan./Feb. 1992.

[69] J. W. Finch, D. J. Atkinson, and P. P. Acarnley, Full-order estimator forinduction motor states and parameters, Proc. Inst. Elect. Eng. Elect.Power Applicat., vol. 145, no. 3, pp. 169179, 1998.

[70] T. Kataoka, S. Toda, and Y. Sato, On-line estimation of induction motorparameters by extended Kalman filter, in Proc. Europe. Conf. PowerElectron. Applicat., vol. 4, 1993, pp. 325329.

[71] A. DellAquila, S. Papa, L. Salvatore, and S. Stasi, A delayed stateKalman filter for online estimation of induction motor parameters androtor flux space vector position, in Proc. IEEE Mediterranean Elec-trotech. Conf., vol. 1, 1996, pp. 269273.

[72] S. Wade, M. W. Dunnigan, and B. W. Williams, Improvements for in-duction machine vector control, in Proc. Europe. Conf. Power Electron.Applicat., 1995, pp. 1.5421.546.

[73] R. S. Pena and G. M. Asher, Parameter sensitivity studies for inductionmotor parameter identification using extended Kalman filter, in Proc.Europe. Conf. Power Electron. Applicat., vol. 4, 1993, pp. 306311.

[74] D. J. Atkinson, J. W. Finch, and P. P. Acarnley, Estimation of rotorresistance in induction motors, Proc. Inst. Elect. Eng.Elect. PowerApplicat., vol. 143, no. 1, pp. 8794, 1996.

[75] D. J. Atkinson, P. P. Acarnley, and J. W. Finch, Parameter identificationtechniques for induction motor drives, in Proc. Europe. Conf. PowerElectron. Applicat., 1989, pp. 307312.

[76] G. G. Soto, E. Mendes, and A. Razek, Reduced-order observers forrotor flux, rotor resistance and speed estimation for vector controlled in-duction motor drives using the extended Kalman filter technique, Proc.Inst. Elect. Eng.Elect. Power Applicat., vol. 146, no. 3, pp. 282288,1999.

[77] T. Du and M. A. Brdys, Algorithms for joint state and parameter es-timation in induction motor drives systems, in Proc. Inst. Elect. Eng.Conf. Contr., 1991, pp. 915920.

[78] T. Du, P. Vas, and F. Stronach, Design and application of extended ob-servers for joint state and parameter estimation in high-performance ACdrives, Proc. Inst. Elect. Eng.Elect. Power Applicat., vol. 142, no. 2,pp. 7178, 1995.

[79] T. Du and M. A. Brdys, Implementation of extended Luenberger ob-servers for joint state and parameter estimation of PWM induction motordrive, in Proc. Europe. Conf. Power Electron. Applicat., vol. 4, 1993,pp. 439444.

[80] M. Koyama, M. Yano, I. Kamiyama, and S. Yano, Micropro-cessor-based vector control system for induction motor drives withrotor time constant identification function, in Proc. IEEE Ind. Applicat.Soc. Annu. Meeting, 1985, pp. 564569.

[81] R. Krishnan and F. C. Doran, A method of sensing line voltages forparameter adaptation of inverter-fed induction motor servo drives, inProc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1985, pp. 570577.

[82] L. J. Garces, Parameter adaption for the speed-controlled static ac drivewith a squirrel-cage induction motor, IEEE Trans. Ind. Applicat., vol.IA-16, pp. 173178, Mar./Apr. 1980.

[83] T. M. Rowan, R. J. Kerkman, and D. Leggate, A simple on-line adaptionfor indirect field orientation of an induction machine, IEEE Trans. Ind.Applicat., vol. 27, pp. 720727, July/Aug. 1991.

[84] M. Sumner, G. M. Asher, and R. Pena, The experimental investiga-tion of rotor time constant identification for vector controlled inductionmotor drives during transient operating conditions, in Proc. Europe.Conf. Power Electron. Applicat., vol. 5, 1993, pp. 5156.

[85] G. Griva, M. C. Ficcara, and F. Profumo, Design of a speed regulatorfor induction motor drives based on model reference robust control, inProc. IEEE Int. Symp. Ind. Electron., 1997, pp. 485488.

[86] S. N. Vukosavic and M. R. Stojic, On-line tuning of the rotor time con-stant for vector-controlled induction motor in position control applica-tions, IEEE Trans. Ind. Electron., vol. 40, pp. 130138, Feb. 1993.

[87] D. Dalal and R. Krishnan, Parameter compensation of indirect vectorcontrolled induction motor drive using estimated airgap power, in Proc.IEEE Ind. Applicat. Soc. Annu. Meeting, 1987, pp. 170176.

[88] D. Y. Ohm, Y. Khersonsky, and J. R. Kimzey, Rotor time constant adap-tation method for induction motors using DC link power measurement,in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1989, pp. 588593.

[89] R. D. Lorenz and D. B. Lawson, A simplified approach to continuous,on-line tuning of field-oriented induction machine drives, IEEE Trans.Ind. Applicat., vol. 26, pp. 420424, May/June 1990.

[90] R. Lessmeier, W. Schumacher, and W. Leonhard, Microprocessor-con-trolled AC-servo drives with synchronous or induction motors: Whichis preferable?, IEEE Trans. Ind. Applicat., vol. IA-22, pp. 812819,Sept./Oct. 1986.

[91] M. P. Kazmierkowski and W. Sulkowski, Transistor inverter-fed induc-tion motor drive with vector control system, in Proc. IEEE Ind. Ap-plicat. Soc. Annu. Meeting, 1986, pp. 162168.

[92] A. A. Ganji and P. Lataire, Rotor time constant compensation of aninduction motor in indirect vector controlled drives, in Proc. Europe.Conf. Power Electron. Applicat., 1995, pp. 1.4311.436.

[93] K. Tungpimolrut, F. Z. Peng, and T. Fukao, Robust vector control ofinduction motor without using stator and rotor circuit time constants,IEEE Trans. Ind. Applicat., vol. 30, pp. 12411246, Sept./Oct. 1994.

[94] L. Umanand and S. R. Bhat, Adaptation of the rotor time constant forvariations in the rotor resistance of an induction motor, in Proc. IEEEPower. Electron. Specialists Conf., 1994, pp. 738743.

[95] F. Loeser and P. K. Sattler, Identification and compensation of the rotortemperature of AC drives by an observer, IEEE Trans. Ind. Applicat.,vol. 21, pp. 13871393, Nov./Dec. 1985.

[96] M. Akamatsu, K. Ikeda, H. Tomei, and S. Yano, High performance IMdrive by coordinate control using a controlled current inverter, IEEETrans. Ind. Applicat., vol. 18, pp. 382392, July/Aug. 1982.

[97] N. R. Klaes, Online tuning of the rotor resistance in an inverter fed in-duction machine with direct-self-control, Europe. Trans. Electr. Power,vol. 4, no. 1, pp. 511, 1994.

[98] K. Ohnishi, Y. Ueda, and K. Miyachi, Model reference adaptive systemagainst rotor resistance variation in induction motor drive, IEEE Trans.Ind. Electron., vol. IE-33, pp. 217223, Aug. 1986.

[99] R. J. Wai, D. C. Liu, and F. J. Lin, Rotor time-constant estimation ap-proaches based on energy function and sliding mode for induction motordrive, Elect. Power Syst. Res., vol. 52, pp. 229239, 1999.

[100] G. Kang, J. Jung, and K. Nam, A new rotor time constant update ruleusing stator flux estimates for an induction motor, in Proc. IEEE Ind.Applicat. Soc. Annu. Meeting, 1999, pp. 12781283.

[101] R. Krishnan and P. Pillay, Sensitivity analysis and comparison ofparameter compensation schemes in vector controlled induction motordrives, in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1986, pp.155161.

[102] A. Dittrich, Parameter sensitivity of procedures for on-line adaptationof the rotor time constant of induction machines with field oriented con-trol, Proc. Inst. Elect. Eng.Elect. Power Applicat., vol. 141, no. 6, pp.353359, 1994.

[103] S. K. Sul, A novel technique of rotor resistance estimation consideringvariation of mutual inductance, IEEE Trans. Ind. Applicat., vol. 25, pp.578587, July/Aug. 1989.

TOLIYAT et al.: A REVIEW OF RFO INDUCTION MOTOR PARAMETER ESTIMATION TECHNIQUES 281

[104] S. Sivakumar, A. M. Sharaf, and K. Natarajan, Improving the perfor-mance of indirect field orientation schemes for induction motor drives,in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1986, pp. 147154.

[105] C. C. Chan and H. Wang, An effective method for rotor resistance iden-tification for high-performance induction motor vector control, IEEETrans. Ind. Electron., vol. 37, no. 6, pp. 477482, 1990.

[106] A. Ba-razzouk, A. Cheriti, and V. Rajagoplan, Real time implementa-tion of a rotor time-constant online estimation scheme, in Proc. IEEEInd. Electron. Soc. Annu. Meeting, 1999, pp. 927932.

[107] H. Toliyat, M. S. Arefeen, K. M. Rahman, and M. Ehsani, Rotor timeconstant updating scheme for a rotor flux oriented induction motordrive, IEEE Trans. Power Electron., vol. 14, pp. 850857, Sept. 1999.

[108] A. Ba-Razzouk, A. Cheriti, and G. Olivier, Artificial neural networksrotor time constant adaptation in indirect field oriented control drives,in Proc. IEEE Power Electron. Specialists Conf., 1996, pp. 701707.

[109] W. Hofmann and Q. Liang, Neural network-based parameter adaptationfor field-oriented ac drives, in Proc. Europe. Conf. Power Electron. Ap-plicat., 1995, pp. 1.3911.396.

[110] D. Fodor, G. Griva, and F. Profumo, Compensation of parameters vari-ations in induction motor drives using a neural network, in Proc. IEEEPower Elect. Specialists Conf., 1995, pp. 13071311.

[111] S. Mayaleh and N. S. Bayindir, On-line estimation of rotor-time con-stant of an induction motor using recurrent neural networks, in Proc.IEEE Workshop Comput. Power Electron., 1998, pp. 219223.

[112] H. T. Yang, K. Y. Huang, and C. L. Huang, An artificial neural networkbased identification and control approach for the field-oriented inductionmotor, Elect. Power Syst. Res., vol. 30, pp. 3545, 1994.

[113] F. Zidani, M. S. Nait-Said, M. E. H. Benbouzid, D. Diallo, andR. Abdessemed, A fuzzy rotor resistance updating scheme for anIFOC induction motor drive, IEEE Power Eng. Rev., pp. 4750,Nov. 2001.

[114] E. Bim, Fuzzy optimization for rotor constant identification of an in-direct FOC induction motor drive, IEEE Trans. Ind. Electron., vol. 48,pp. 12931295, Dec. 2001.

[115] J. Soltani and B. Mirzaeian, Simultaneous speed and rotor time constantidentification of an induction motor drive based on the model referenceadaptive system combined with a fuzzy resistance estimator, in Proc.IEEE Power Electron. Drives Energy Syst. Ind. Growth Conf., 1998, pp.739744.

[116] A. Cataliotti and G. Poma, A fuzzy-logic control system for an induc-tion motor drive compensating the variation of rotor resistance, in Proc.Power Electron. Motion Contr. Conf., 1998, pp. 5.2135.217.

[117] P. Vas, Artificial-Intelligence-Based Electrical Machines andDrives. Oxford: Oxford Univ. Press, 1999.

[118] L. Zhen and L. Xu, On-line fuzzy tuning of indirect field oriented in-duction motor drive, in Proc. IEEE Appl. Power Elec. Conf., 1996, pp.369374.

[119] E. Cerruto, A. Consoli, A. Raciti, and A. Testa, Fuzzy adaptive vectorcontrol of induction motor drives, IEEE Trans. Power Electron., vol.12, no. 6, pp. 10281040, Nov. 1997.

[120] M. Ta-Cao and H. Le-Huy, Rotor resistance estimation using fuzzylogic for high performance induction motor drives, in Proc. IEEE Ind.Electron. Soc. Annu. Meeting, 1998, pp. 303308.

[121] C. Attaianese, I. Marongiu, and A. Perfetto, A speed sensorlessdigitally controlled induction motor drive estimating rotor resistancevariation, in Proc. Europe. Conf. Power Electron. Applicat., 1995, pp.1.7411.746.

[122] C. Attaianese, G. Tomasso, A. Damiano, I. Marongiu, and A. Perfetto,On line estimation of speed and parameters in induction motor drives,in Proc. IEEE Int. Symp. Ind. Electron., 1997, pp. 10541059.

[123] A. Kelemen, T. Pana, and F. Stugren, Implementation of sensorlessvector-controlled induction motor drive system with rotor resistance es-timation using floating point DSP, in Proc. Power Electron. MotionContr. Conf., 1996, pp. 2.1862.191.

[124] S. H. Lee, I. J. Ha, H. S. Yoo, B. Y. Hong, and S. J. Yoon, An on-lineidentification method for both stator and rotor resistances of inductionmotors without rotational transducers, in Proc. IEEE Int. Symp. Ind.Electron., 1996, pp. 325330.

[125] T. Pana, Sensorless vector-controlled induction motor drive systemwith rotor resistance estimation using parallel processing with floatingpoint DSP, in Proc. Power Conversion Conf., 1997 , pp. 7984.

[126] N. Hur, K. Hong, and K. Nam, Real-time parameter identificationscheme for the sensorless control of induction motors using a reducedorder model, in Proc. IEEE Ind. Electron. Soc. Annu. Meeting, 1996,pp. 11611166.

[127] H. Kubota and K. Matsuse, Speed sensorless field-oriented control ofinduction motor with rotor resistance adaptation, IEEE Trans. Ind. Ap-plicat., vol. 30, pp. 12191224, Sept./Oct. 1994.

[128] J. Faiz and M. B. B. Sharifian, Different techniques for real time esti-mation of an induction motor rotor resistance in sensorless direct torquecontrol for electric vehicle, IEEE Trans. Energy Conversion, vol. 16,pp. 104109, Mar. 2001.

[129] K. Akatsu and A. Kawamura, Online rotor resistance estimation usingthe transient state under the speed sensorless control of induction motor,IEEE Trans. Power Electron., vol. 15, pp. 553560, May 2000.

[130] V. Ambrozic, D. Nedeljkovic, and J. Nastran, Sensorless control of in-duction machine with parameter adaptation, in Proc. IEEE Int. Symp.Ind. Electron., 1999, pp. 724728.

[131] K. Akatsu and A. Kawamura, Sensorless very low and zero speed esti-mations with on-line secondary resistance estimation of induction motorwithout adding any signal, in Proc. IEEE Ind. Applicat. Soc. Annu.Meeting, 1999, pp. 187193.

[132] V. Ambrozic, J. Nastran, and D. Nedeljkovic, Induction machinesensorless control with stator and rotor resistance adaptation, in Proc.IEEE Appl. Power Electron. Conf., 1999, pp. 12371243.

[133] H. Kubota, D. Yoshihara, and K. Matsuse, Rotor resistance adaptationfor sensorless vector-controlled induction machines, Elect. Eng. Japan,vol. 125, no. 2, pp. 6572, 1998.

[134] S. Y. Lin, H. Wu, and Y. T. Tzou, Sensorless control of induction motorswith on-line rotor time constant adaptation, in Proc. IEEE Power Elect.Specialists Conf., 1998, pp. 15931598.

[135] R. Krishnan and A. S. Bharadwaj, A review of parameter sensitivity andadaptation in indirect vector controlled induction motor drive systems,IEEE Trans. Power Electron., vol. 6, pp. 695703, Oct. 1991.

[136] L. Umanand and S. R. Bhat, Online estimation of stator resistance of aninduction motor for speed control applications, Proc. Inst. Elect. Eng.Elect. Power Applicat., vol. 142, no. 2, pp. 97103, 1995.

[137] R. J. Kerkman, B. J. Seibel, T. M. Rowan, and D. Schlegel, A new fluxand stator resistance identifier for ac drive systems, in Proc. IEEE Ind.Applicat. Soc. Annu. Meeting, 1995, pp. 310318.

[138] E. Akin, H. B. Ertan, and M. Y. Uctug, A method for stator resistancemeasurement suitable for vector control, in Proc. IEEE Ind. Electron.Soc. Annu. Meeting, 1994, pp. 2122-2126.

[139] J. Weidauer and A. Dittrich, A new adaptation method for inductionmachines with field-oriented control, in Proc. Europe. Conf. PowerElectron. Applicat., 1991, pp. 2.1512.156.

[140] R. Blasco-Gimenez, G. M. Asher, and M. Sumner, A new method ofstator resistance estimation for enhanced dynamic performance of sen-sorless vector control drives, in Proc. Europe. Conf. Power Electron.Applicat., 1995, pp. 1.6891.694.

[141] M. Rodic and K. Jezernik, Speed sensorless sliding mode torqueand flux control of induction motor with stator resistance adaptationalgorithm, in Proc. Europe. Conf. Power Electron. Applicat., 2001,[CD-ROM] Paper no. pp01010.

[142] M. Tsuji, S. Chen, K. Izumi, and E. Yamada, A sensorless vector con-trol system for induction motors using q-axis flux with stator resistanceidentification, IEEE Trans. Ind. Electron., vol. 48, pp. 185194, Feb.2001.

[143] H. Tajima, G. Guidi, and H. Umida, Consideration about problems andsolutions of speed estimation method and parameter turning for speedsensorless vector control of induction motor drives, in Proc. IEEE Ind.Applicat. Soc. Annu. Meeting, 2000, pp. 17871793.

[144] R. Marino, S. Peresada, and P. Tomei, On-line stator and rotorresistance estimation for induction motors, IEEE Trans. Contr. Syst.Technol., vol. 8, no. 3, pp. 570579, May 2000.

[145] G. Guidi and H. Umida, A sensorless induction motor drive for lowspeed applications using a novel stator resistance estimation method,in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1999, pp. 180186.

[146] V. Vasic and S. Vukosavic, Robust MRAS-based algorithm for statorresistance and rotor speed identification, IEEE Power Eng. Rev., pp.3941, Nov. 2001.

[147] J. Cambell and M. Sumner, An artificial neural network for stator resis-tance estimation in a sensorless vector controlled induction motor drive,in Proc. Europe. Conf. Power Electron. Applicat., 2001, [CD-ROM]Paper no. pp00917.

[148] J. L. Zamora and A. G. Cerrada, On-line estimation of the statorparameters in an induction machine using only voltage and currentmeasurements, in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting,1999, pp. 805816.

282 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 2, JUNE 2003

[149] K. Shinohara, T. Nagano, H. Arima, and W. Z. W. Mustafa, Onlinetuning method of stator and rotor resistances in both motoring and re-generating operations for vector controlled induction machines, Elect.Eng. Japan, vol. 135, no. 1, pp. 5664, 2001.

[150] E. D. Mitronikas, A. N. Safacas, and E. C. Tatakis, A new stator resis-tance tuning method for stator-flux-oriented vector-controlled inductionmotor drive, IEEE Trans. Ind. Electron., vol. 48, pp. 11481157, Dec.2001.

[151] L. A. Cabrera, E. Elbuluk, and I. Husain, Tuning the stator resistanceof induction motors using artificial neural network, IEEE Trans. PowerElectron., vol. 12, pp. 779787, Sept. 1997.

[152] B. K. Bose and N. R. Patel, Quazi-fuzzy estimation of stator resistanceof induction motor, IEEE Trans. Power Electron., vol. 13, pp. 401409,May 1998.

[153] L. Zhong, M. F. Rahman, K. W. Lim, Y. W. Hu, and Y. Xu, A fuzzyobserver for induction motor stator resistance for application in directtorque control, in Proc. IEEE Int. Power Electron. Drive Syst. Conf.,1997, pp. 9196.

[154] Y. S. Lai, J. C. Lin, and J. J. Wang, Direct torque control inductionmotor drives with self-commissioning based on Taguchi methodology,IEEE Trans. Power Electron., vol. 15, pp. 10651071, Nov. 2000.

[155] Y. S. Lai, J. C. Lin, and J. Wang, Self-commission of induction motordrives based on direct torque control, in Proc. IEEE Power Eng. Soc.Summer Meeting, 2000, pp. 24812486.

[156] S. Mir, M. E. Elbuluk, and D. S. Zinger, PI and fuzzy estimatorsfor tuning the stator resistance in direct torque control of inductionmachines, IEEE Trans. Power Electron., vol. 13, pp. 279287, Mar.1998.

[157] T. G. Habetler, F. Profumo, G. Griva, M. Pastorelli, and A. Bettini,Stator resistance tuning in a stator-flux field-oriented drive using aninstantaneous hybrid flux estimator, IEEE Trans. Power Electron., vol.13, pp. 125133, Jan. 1998.

[158] B. S. Lee and R. Krishnan, Adaptive stator resistance compensator forhigh performance direct torque controlled induction motor drives, inProc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1998, pp. 423430.

[159] A. Monti, F. Pironi, F. Sartogo, and P. Vas, A new state observer for sen-sorless DTC control, in Proc. Inst. Elect. Eng. Power Electron. VariableSpeed Drives Conf., 1998, pp. 311317.

[160] P. Tiitinen, The next generation motor control method, DTC directtorque control, in Proc. IEEE Int. Conf. Power Electron., Drives En-ergy Syst. Ind. Growth , 1996, pp. 3743.

[161] J. Faiz, S. H. Hossieni, M. Ghaneei, A. Keyhani, and A. Proca, Di-rect torque control of induction motors for electric propulsion systems,Elect. Power Syst. Res., vol. 51, pp. 95101, 1999.

[162] Y. W. Hu, L. Zhong, M. F. Rahman, K. W. Lim, Y. Xu, M. A. Rahman,and A. R. Bhuiya, Direct torque control of an induction motor usingfuzzy logic, in Proc. IEEE Can. Conf. Elect. Comput. Eng., 1997, pp.767772.

[163] B. Dobrucky, F. Chaloupka, P. Kucer, and V. Racek, A neural fuzzycontrol with stator resistance estimator for IM drives with DSC, in Proc.Europe. Conf. Power Electron. Applicat., 1995, pp. 1.7891.794.

[164] Z. Krzeminski, Differential equations of induction motor with non-linear control synthesis with regard to saturation of main magnetic path,Rozprawy Elektrotechnicki, vol. 34, no. 1, pp. 117131, 1988.

[165] E. Levi and Z. Krzeminski, Main flux saturation modeling indq axis models of induction machines using mixed current-fluxstate-space models, Europe. Trans. Elect. Power, vol. 6, no. 3, pp.207215, 1996.

[166] E. Levi, A unified approach to main flux saturation modeling in dqaxis models of induction machines, IEEE Trans. Energy Conversion,vol. 10, pp. 455461, Sept. 1995.

[167] E. Levi and V. Vuckovic, Rotor flux computation in saturated field-oriented induction machines, Elect. Mach. Power Syst., vol. 21, no. 6,pp. 741754, 1993.

[168] E. Levi, Magnetic saturation in rotor flux oriented induction motordrives: Operating regimes, consequences and open-loop compensation,Europe. Trans. Elect. Power, vol. 4, no. 4, pp. 277286, 1994.

[169] E. Levi and M. Sokola, A novel saturation adaptive rotor flux esti-mator for rotor flux oriented induction machines, in Proc. Europe. Conf.Power Electron. Applicat., 1997, pp. 1.5181.523.

[170] E. Levi, S. Vukosavic, and V. Vuckovic, Saturation compensationschemes for vector controlled induction motor drives, in Proc. IEEEPower Elect. Specialists Conf., 1990, pp. 591598.

[171] Z. Krzeminski and A. Jaderko, Main magnetic path saturation effect inobserver system of the induction motor, in Proc. IEEE Int. Symp. Ind.Electron., 1993, pp. 753758.

[172] J. S. Choi, W. M. Kim, and Y. S. Kim, Speed sensorless control ofinduction motor considering the flux saturation, in Proc. IEEE Appl.Power Electron. Conf., 2000, pp. 148153.

[173] Y. J. Kim, J. S. Choi, and Y. S. Kim, Speed sensorless control of asaturated induction motor using a hybrid speed estimator, in Proc. IEEETelecomm. Energy Conf., 1999, pp. 367370.

[174] D. H. Choi, S. B. Cho, and D. S. Hyun, Improved torque responseby tuning of the magnetizing inductance under field weakening oper-ation region, in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1997,pp. 418425.

[175] J. S. Choi, Y. S. Han, and Y. S. Kim, A new speed estimation scheme ofthe induction motor considering the flux saturation, IEEE Trans. Mag-netics, vol. 36, pp. 35953598, Sept. 2000.

[176] H. Y. Kim, M. S. Shin, and D. S. Hyun, On-line tuning of parametervariations of induction motor for high performance drives, in Proc.IEEE Ind. Applicat. Soc. Annu. Meeting, 1998, pp. 431437.

[177] Z. Krzeminski and A. Jaderko, A speed observer system of inductionmotor with magnetizing curve identification, in Proc. Int. Conf. PowerElectron. Motion Contr., 2000, pp. 6.1096.113.

[178] C. Mastorocostas, Rotor time constant estimation in induction motordrives, in Proc. Int. Aegean Conf. Elect. Mach. Power Electron., 2001,pp. 562566.

[179] D. Balara and J. Timko, Idetification of induction motor parameterswith use of neural networks taking into account main flux saturationeffect, in Proc. Int. Conf. Power Electron. Motion Contr., 2000, pp.6.176.22.

[180] A. Jaderko and Z. Krzemisnki, Fuzzy-logic algorithm of the mutualinductance identification in nonlinear observer system of the inductionmachine, in Proc. Int. Conf. Power Electron. Motion Contr., 1996, pp.481485.

[181] E. Levi and M. Wang, A speed estimator for sensorless vector control ofinduction machines in the field weakening region, in Proc. IEEE PowerElectron. Specialists Conf., 2000, pp. 897902.

[182] R. C. Healey, S. Williamson, and A. C. Smith, Improved cage rotormodels for vector controlled induction motors, IEEE Trans. Ind. Ap-plicat., vol. 31, pp. 812822, July/Aug. 1995.

[183] R. C. Healey, The implementation of a vector control scheme basedon an advanced motor model, in Proc. Europe. Conf. Power Electron.Applicat., 1995, pp. 3.0173.022.

[184] S. Williamson and R. C. Healey, Space vector representation of ad-vanced motor models for vector controlled induction motors, Proc. Inst.Elect. Eng.Elect. Power Applicat., vol. 143, no. 1, pp. 6977, 1996.

[185] M. Wang, E. Levi, and M. Jovanovic, Compensation of parameter vari-ation effects in sensorless indirect vector controlled induction machinesusing model based approach, Elect. Mach. Power Syst., vol. 27, no. 9,pp. 10011027, 1999.

[186] H. Kubota and K. Matsuse, Compensation of core loss of adaptive fluxobserver-based field-oriented induction motor drives, in Proc. IEEEInd. Electron. Soc. Annu. Meeting, 1992, pp. 6771.

[187] K. Matsuse, S. Taniguchi, T. Yoshizumi, and K. Namiki, A speed-sen-sorless vector control of induction motor operating at high efficiencytaking core loss into account, IEEE Trans. Ind. Applicat., vol. 37, pp.548558, Mar./Apr. 2001.

[188] D. Hintze and D. Schrder, Induction motor drive with intelligent con-troller and parameter adaption, in Proc. IEEE Ind. Applicat. Soc. Annu.Meeting, 1992, pp. 970977.

[189] T. Mizuno, J. Takayama, T. Ichioka, and M. Terashima, Decouplingcontrol method of induction motors taking core loss into consideration,in Proc. Int. Power Electron. Conf., 1990, pp. 6974.

[190] D. Hofmeyer, Torque linearity for high speed drives with a wide fieldweakening range, in Proc. Europe. Conf. Power Electron. Applicat.,2001, [CD-ROM] Paper no. pp00431.

[191] O. Touhami, R. Ibtiouen, S. Moulahoum, and S. Mekhtoub, Analysisand compensation of parameter variation effect in vector controlled in-duction machine, Archives of Electrical Engineering, vol. 50, no. 2, pp.165182, 2001.

[192] K. Namiki, K. Matsuse, T. Kishimoto, and S. Yokomizo, Characteris-tics of speed sensorless vector controlled induction motor drive oper-ating at high efficiency taking core loss into account, in Proc. IEEEAppl. Power Electron. Conf., 2001, pp. 2531.

[193] J. Jung and K. Nam, A vector control scheme for EV induction motorswith a series iron loss model, IEEE Trans. Ind. Electron., vol. 45, pp.617624, Aug. 1998.

[194] K. Matsuse, T. Yoshizumi, S. Katsuta, and S. Taniguchi, High-responseflux control of direct-field-oriented induction motor with high efficiencytaking core loss into account, IEEE Trans. Ind. Applicat., vol. 35, pp.6269, Jan./Feb. 1999.

TOLIYAT et al.: A REVIEW OF RFO INDUCTION MOTOR PARAMETER ESTIMATION TECHNIQUES 283

[195] S. D. Wee, M. H. Shin, and D. S. Hyun, Stator-flux-oriented control ofinduction motor considering iron loss, IEEE Trans. Ind. Electron., vol.48, pp. 602608, June 2001.

[196] S. Shinnaka, Proposition of new mathematical models with stator coreloss factor for induction motor, Elect. Eng. Japan, vol. 134, no. 1, pp.6475, 2001.

[197] M. Abdel-Salam, S. Abou-Shadi, and Y. Sayed, Analysis of inductionmotors fed from constant current source taking core-loss and satura-tion into consideration, Elect. Mach. Power Syst., vol. 27, no. 6, pp.581599, 1999.

[198] J. Jung, K. Nam, C. Han, J. Chung, and D. Ahn, A new vector controlscheme considering iron loss for electric vehicle induction motors, inProc. IEEE Ind. Applicat. Soc. Annu. Meeting, 1997, pp. 439444.

[199] W. Choi, D. W. Chung, and S. K. Sul, Implementation of field orientedinduction machine considering iron losses, in Proc. IEEE Appl. PowerElectron. Conf., 1996, pp. 375379.

[200] T. Noguchi and D. Hiraishi, Core loss compensation of direct field-oriented induction motor incorporating robust parameter identification,in Proc. IEEE Ind. Electron. Soc. Annu. Meeting, 1999, pp. 14091414.

[201] S. Shinnaka, Proposition of new mathematical models with core lossfactor for controlling AC motors, in Proc. IEEE Ind. Electron. Soc.Annu. Meeting, 1998, pp. 297302.

[202] T. Noguchi, P. Nakmahachalasint, and N. Watanakil, Precise torquecontrol of induction motor with on-line parameter identification in con-sideration of core loss, in Proc. Power Conversion Conf., 1997, pp.113118.

[203] C. Millet, D. Leroux, Y. Li, B. Feuvrie, and C. Bergmann, Modernautomatic tools for an identification and a field-oriented control designof an induction machine: A no linear decoupling structure, in Proc.IEEE Power Electron. Specialists Conf., 1997, pp. 14861492.

[204] M. Hasegawa, H. Yamasaki, S. Doki, and S. Okuma, Compensation ofstator iron loss of vector-controlled induction motor using robust fluxobserver, Elect. Eng. Japan, vol. 137, no. 3, pp. 5966, 2001.

[205] V. Valouch, Induction motor field-oriented controller, respecting ironlosses, in Proc. IEEE Int. Symp. Ind. Electron., 1995, pp. 668673.

[206] H. Rasmussen, P. Vadstrup, and H. Borsting, Rotor field oriented con-trol with adaptive iron loss identification, in Proc. IEEE Ind. Applicat.Soc. Annu. Meeting, 1999, pp. 12531258.

[207] A. Dittrich, Model based identification of the iron loss resistance ofan induction machine, in Proc. Inst. Elect. Eng. Conf. Power Electron.Variable Speed Drives, 1998, pp. 500503.

Hamid A. Toliyat (S87M91SM96) received thePh.D. degree in electrical engineering from the Uni-versity of Wisconsin-Madison in 1991.

Currently, he is an Associate Professor in theDepartment of Electrical Engineering at Texas A&MUniversity, College Station. Dr. Toliyat is an Editorof IEEE TRANS. ENERGY CONVERSION, an AssociateEditor of IEEE TRANS.POWER ELECTRONICS, anda member of the Editorial Board of Electric PowerComponents and Systems Journal. His main researchinterests and experience include multiphase variable

speed drives, fault diagnosis of electric machinery, analysis and design ofelectrical machines, and sensorless variable speed drives. He has publishedover 150 technical papers in these fields. He is actively involved in presentingshort courses and consulting in his area of expertise to various industries.

He has received the Texas A&M Select Young Investigator Award in 1999,Eugene Webb Faculty Fellow Award in 2000, NASA Space Act Award in 1999,and the Schlumberger Foundation Technical Award in 2000 and 2001. He is alsoVice-Chairman of IEEE-IAS Electric Machines Committee, and is a memberof Sigma Xi. He is the recipient of the 1996 IEEE Power Eng. Society PrizePaper Award for his paper on the Analysis of Concentrated Winding InductionMachines for Adjustable Speed Drive ApplicationsExperimental Results.

Emil Levi (S89M92SM99) was born in 1958 inZrenjanin, Yugoslavia. He received the Diploma de-gree from the University of Novi Sad, Yugoslavia,and the M.Sc. and Ph.D. degrees in electrical engi-neering from the University of Belgrade, Yugoslavia,in 1982, 1986, and 1990, respectively.

Currently, he is Professor of Electric Machinesand Drives in the School of Engineering at LiverpoolJohn Moores University, Liverpool, U.K. In 1982,he joined the Department of Electrical Engineeringat the University of Novi Sad, where he became

Assistant Professor in 1991. He joined Liverpool John Moores University,U.K., in May 1992 as a Senior Lecturer. From 1995 till 2000, he was a Readerin Electrical Power Engineering. His main areas of research interest aremodeling and simulation of electric machines, control of high performancedrives, and power electronic converters. He has published over 130 papers,including more than 30 papers in major international journals.

Mona Raina (S00) received the Bachelors degreein electrical engineering from the University ofMadras, India. She is currently pursuing the M.Sc.degree in electrical engineering at Texas A&MUniversity, College Station.

She is currently with Novellus Systems, Inc., SanJose, CA. Her research interests are in the fields ofpower electronics and motor drives and they includethe parameter estimation of induction motors.

Index:

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