Simulation Modelling Practice and Theory...Modeling and simulation of three-phase squirrel cage...

Simulation Modelling Practice and Theory 18 (2010) 277–290

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Simulation Modelling Practice and Theory

journal homepage: www.elsevier .com/ locate/s impat

Hardware-In-the-Loop for on-line identification and controlof three-phase squirrel cage induction motors

Ashraf Saleem a,*, Rateb Issa b, Tarek Tutunji a

a Mechatronics Engineering Department, Faculty of Engineering, Philadelphia University, Jordanb Mechatronics Engineering Department, Faculty of Engineering, Balqa’ Applied University, Jordan

a r t i c l e i n f o a b s t r a c t

Article history:Received 8 June 2009Received in revised form 28 September2009Accepted 9 November 2009Available online 17 November 2009

Keywords:Identification and controlInduction motorHardware-In-the-LoopARMA models

1569-190X/$ - see front matter � 2009 Elsevier B.Vdoi:10.1016/j.simpat.2009.11.002

* Corresponding author. Address: P.O. Box 1, PhilE-mail address: [email protected] (A.

This paper describes a strategy for identification and control of three-phase squirrel cageinduction motors. The strategy in this work is divided into 3 stages: on-line identification,off-line controller design, and on-line control. First, the transfer function is identified on-line. Next, the controller design is performed in a pure simulation environment usingthe identified transfer function. Finally, the designed controller is applied to the real sys-tem.

Simulation and experimental results are presented to show the validity of the proposedstrategy. Advantages of the proposed strategy include high accuracy in the identified sys-tem, simplicity, and low cost.

� 2009 Elsevier B.V. All rights reserved.

1. Introduction

Due to their simple structure, reliability of operation and modest cost, the squirrel cage induction motors are the mostwidely used electrical drive motors. The progress of semiconductor technology in the last years made it possible for staticconverters to be built at acceptable price, and therefore induction motors has a future in variable speed drives. In inductionmotors, specific demands are required at steady state and transient state operation. These demands depend on load type andduty cycle of the motor. The steady state condition is treated as a special case of the more general solution [1].

The theory of the induction motor for dynamic conditions is somewhat involved because of the rotating magnetic fields,the spatial relationships of which depend on speed and load. On the other hand, the equivalent circuit usually derived forsteady state operation with sinusoidal voltages proves to be inadequate when dealing with transients or when the motoris supplied from a static converter.

The mathematical model to be used is tailored to the needs of controlled drives. It incorporates most of the qualitativefeatures of an actual motor but would not, be accurate enough for design purposes [2]. Induction motors exhibit nonlineardynamic behavior and therefore it is a challenge to establish an adequate mathematical model for controller design pur-poses. Substantial research in the past decades focused on the derivation of suitable mathematical models in order to designappropriate controllers for these motors [3–5].

Linear regression models have been generally used to approximate the behavior of nonlinear systems. Some of thesemodels use the black box concept to map input/output data patterns using adaptive mathematical functions. Researchershave worked on identification of induction motor parameters. Koubaa [6] and Castaldi et al. [7] used a recursive predictionerror method and adaptive observers to estimate the motor parameters, such as the rotor resistance, rotor inductance, and

. All rights reserved.

adelphia University, 19392, Jordan. Tel.: +962 799979892; fax: +962 64799037.Saleem).

http://dx.doi.org/10.1016/j.simpat.2009.11.002mailto:[email protected]://www.sciencedirect.com/science/journal/1569190Xhttp://www.elsevier.com/locate/simpat

Nomenclature

vds d-axis component of the stator voltage, Vvqs q-axis component of the stator voltage, Vids d-axis component of the stator current, Aidq q-axis component of the stator current, Ai0dr d-axis component of the rotor current referred to the stator, Ai0qr q-axis component of the rotor current referred to the stator, ALs stator inductance, HL0r rotor inductance referred to the stator, HR1 stator winding resistanceR02 rotor winding resistanceLm mutual inductance between rotor and stator, HLss Ls + Lm, HL0rr L

0r þ Lm, H

xs stator electrical angular speed, rad/sxr rotor electrical angular speed, rad/sxm 2P xr , rotor mechanical angular speed, rad/sP number of polesTe electromagnetic torque, N mTm load torque, N mJ equivalent moment of inertia, kg m2

278 A. Saleem et al. / Simulation Modelling Practice and Theory 18 (2010) 277–290

stator leakage inductance of a three-phase induction machine. Their work used a complicated setup experiment based on aDSP tool. Moreover, their aim was to estimate the motor parameters rather than identify the transfer function.

The Hardware-In-the-Loop is an environment where virtual components work in conjunction with real system’s compo-nents. It is mainly employed to test a real control system on a virtual plant in order to verify its performance before applyingit to the real plant [8,9].

In this research, identification methods based on linear regression methods are utilized on-line to develop transfer func-tions of induction motors. These functions are then used to design appropriate controllers under simulation environment.Once verified, the controller is applied to the real AC motors. The on-line identification and control are performed usingthe HIL concept.

The 3-stage strategy proposed and implemented in this paper contributes to the field of identification and control ofinduction motors by clearly presenting organized steps for testing and applying controllers. Other researchers conductedwork in the parameter estimation, model identification, and controller design of induction motors [3–7]. The implementa-tion of Hardware-In-the-Loop (HIL) within the well defined strategy given in this paper is an added value to the researchcommunity.

In previous works, system identification methods were developed and applied to different engineering systems. Tutunjiet al. [10] used impulse response data in a recursive gradient algorithm to identify the transfer function of a DC motor andgyroscope. Abedrabbo and Tutunji [11] presented identification model and sensitivity analysis of hydrostatic transmissionsystem. Saleem et al. [12] applied identification and control to a pneumatic servo drives using mixed-reality environment.

This paper is divided as follows: Section 2 gives a brief overview of induction motors model. Section 3 presents the systemidentification and control and (Section 3.2) explains HIL concepts used. (Section 3.3) demonstrates the proposed strategy.Section 4 presents the experimental results and Section 5 concludes the paper.

2. Modeling and simulation of three-phase squirrel cage induction motor

Many studies of the transient and steady state performance of induction motors have used two axes (d–q) dynamic ma-chine model for the solution of the motor performance equations [13,14], while other studies have used a direct three-phasedynamic model that seemed more convenient, due to the variables involved in such modeling, in which they are the actualphysical quantities of the motor [15]. Some authors have used dynamic model for small perturbations and transfer function,or solutions for dynamic behavior in complex symbolic form [16].

The steady state performances of the induction motors are obtained using static model equations, derived from a dynamicmodel by setting their derivatives to zero and solving the resulting motor equations for the motor variables.

The state-space model of induction motor in standard form, with respect to a synchronously rotating d–q coordinates[17,18], is as follows:

x ¼ ids i0dr iqs i0qr xm

h iT— state vector

A. Saleem et al. / Simulation Modelling Practice and Theory 18 (2010) 277–290 279

Y ¼ ids i0dr iqs i0qr xm Te

h iT— output vector

u ¼ vds 0 vqs 0 Tm½ � — input vectorD ¼ ½0� — direct transmission matrix

A ¼

xs þ P2 xmK2 P2 xmK1LmLss

R1Lss

K1 � R02

LmK2 0

R1Lss

K1 � R02

LmK2 �xs � P2 xmK2 � P2 xmK1

LmLss

0

� P2 xmK1LmL0rr

xs � P2 xmK1 �R1Lm

K2R02L0rr

K1 0

� R1Lm K2R02L0rr

K1 P2 xmK1LmL0rr

�xs þ P2 xmK1 0

� aLmi0qr

J 0aLmi0dr

J 0 0

266666666664

377777777775

— state matrix

B ¼

K1Lss

0 � K2Lm 0 00 K1Lss 0 �

K2Lm

0

� K2Lm 0K1L0rr

0 0

0 � K2Lm 0K1L0rr

0

0 0 0 0 � 1J

2666666664

3777777775� input matrix

C ¼

1 0 0 0 00 1 0 0 00 0 1 0 00 0 0 1 00 0 0 0 1

�aLmi0qr 0 aLmi0dr 0 0

2666666664

3777777775

— output matrix

a ¼ 34

P — constant

K1 ¼LssL

0rr

LssL0rr � L

2m

— constant

K2 ¼L2m

LssL0rr � L

2m

� constant

Referring to the described state-space model of induction motor, a Simulink drive system block diagram was built in order tosimulate the dynamic behavior as shown in Fig. 1.

The parameters of the induction motor may change during the operation of the drive system, causing deviations betweenthe corresponding signals of the model and the motor. The stator resistance and rotor resistance change with temperature.This is a relatively slow process as the thermal time constants are large. Parameters changes produced by magnetic satura-tion affect the stator reactance, rotor reactance and mutual reactance to some extent [28].

The induction motor parameters were determined by testing the motor under no-load and locked rotor conditions [35].The real parameters of the induction motor were used in the simulated system and are given in Table 1.

Fig. 2 shows the speed response comparison between the mathematical model and the real system for step, trapezoidal,and multi-trapezoidal inputs respectively. Results reveal that the mathematical model was able to follow the real systemwith negligible steady state error.

Fig. 1. Simulink model of the induction motor.

Table 1Induction motor data.

Parameter Value

Stator resistance R1 17.22 XStator reactance X1 19.2 XMutual reactance Xm 252.6 XRotor resistance referred to the stator R02 18.69 X

Rotor reactance referred to the stator X02 19.2 XNominal voltage V1n 220/380 VNominal speed nn 2870 rpmNominal output power P2N 300 WNominal current I1n 0.8 APower factor cos u1n 0.74Nominal frequency f1n 50 HzNumber of poles P 2Moment of inertia J 0.02 kg m2


3. System identification and control through HIL

This section describes well known induction motors identification and control techniques. Furthermore, it presents theproposed strategy that based on HIL for the identification and control of induction motors.

3.1. System identification

Mathematical models can be constructed using analytical approach, such as physics laws, or using experimental ap-proach. The later is usually used when critical information related to the system is missing.

System identification is the field of approximating dynamic system models from input/output patterns acquired throughphysical experiments. This includes the experimental setup, data acquisition, determination of an appropriate model, andthe design of an algorithm for parameter convergence [9].

The system is driven by input ‘‘control” variables u(t), with added noise v(t), to give an output y(t).The target is to establish a mathematical model that mimics the original system and therefore minimizes the error be-

tween the system and model outputs. System identification incorporates the following steps:

1. Experiment design. This includes the choice of lab equipment to be used such as computers, DAQ, and interface.2. Model structure determination. The choice of the model can range from nonparametric models, such as frequency analysis

and fuzzy, to parametric methods, such as difference equations and neural networks.3. Experiment run. This is usually done by exciting the system with an input signal (pulse, sinusoid, or random) and measur-

ing the output signal over a specified time interval.4. Algorithm choice and run. The algorithm used for convergence can vary from simple one-shot least squares, recursive least

squares to advanced multi-structures such as back propagation.5. Validation of results. The output of the identified model is compared to the original system through different and ‘new’

input signals.

The identification is referred to on-line when steps 3 and 4 are done concurrently and off-line when the results of step 3are recorded and at a later stage step 4 is applied to the collected data.

Researchers have applied several models and algorithms to identify dynamic induction motor dynamics. Koubaa [6] useda linear parameter estimation technique, based on recursive least squares, to determine the rotor resistance, self inductanceof the rotor winding, and the stator leakage inductance of a three-phase induction. The model developed was based on stea-dy-state equations of induction motor dynamics. Huang et al. [26] applied genetic algorithms for parameter identification offield orientation control induction motors. The model’s parameters were estimated using the motor’s dynamic response to adirect on-line start. Burton et al. [27] used neural network models and an algorithm based on random search of the errorsurface gradient to identify and control induction motor stator currents. A comparison to the backpropagation algorithmwas also provided.

Holtz and Thimm [28] applied on-line identification technique based on the evaluation of the dynamic response of theinduction motor to a Pulse Width Modulation switch sequence to estimate the rotor time constant and other machineparameters. Once the critical parameters were estimated, a control method based on field oriented control was applied toa vector-controlled induction motor drive. Attaianese et al. [29] presented two on-line identifiers, based on the model ref-erence adaptive control theory, for the rotor parameters of the field-oriented induction motor drive. The experimental re-sults used DSP system. Levi and Wang [30] proposed a method for on-line mutual inductance identification in vector-controlled induction machines.

0 5 10 15 20 25 30 35 40 45 500

0.5

1

1.5

2

2.5

3

3.5

4

Time

Spee

d (v

)

Math Model

Real Output

0 50 100 150-0.5

0

0.5

1

1.5

2

2.5

3

Real System

Math model

Spee

d (v

)

Time

0 100 200 300 400 500 6000

0.5

1

1.5

2

2.5

3

Time

Spee

d (v

)

Comparison of the mathematical model and the real system responses to a multi trapezoidal input signal

Math Model

RealSystem

a b

c

Fig. 2. Mathematical model vs. real system responses to (a) step, (b) trapezoidal and (c) Multi-trapezoidal inputs (speed scale = 0.9 mv/rpm).

OutputInput

SystemModel

Delay

Delay

Delay

Delay

Fig. 3. ARMA model general structure.


In this paper, the identification models used are based on Auto-Regressive Moving-Average (ARMA) models. ARMAmodels are difference equation models that map input–output data. The general structure for an ARMA model is given inFig. 3.

The output is a linear difference equation of current and past inputs and past outputs. This ARMA equation is given next

ŷk ¼Xnj¼1

ajyk�j þXmi¼0

biuk�i ð1Þ

where uk and yk are the inputs and outputs at discrete-time k, and aj and bi are the ARMA parameters.


The Z-Transform of the ARMA(m, n) model is calculated to yield the transfer function of the model which is given by

Z yk �Xnj¼1

ajyk�j

( )¼ Z

Xmi¼0

biuk�i

( )ð2Þ

) HðzÞ ¼ YðzÞUðzÞ ¼

b0 þ b1z�1 þ � � � þ bmz�m1� a1z�1 � � � � � anz�n

ð3Þ

The main advantage of using ARMA models is the direct mapping of those models into transfer functions. Once the trans-fer function is established, they can replace the system in designing controllers in a simulated environment.

The target is to minimize the Sum Square Error (SSE) between the system and ARMA model outputs as given by

E ¼ 12

XKk¼1ðŷk � ykÞ

2 ð4Þ

In this paper, the recursive least square (RLS) algorithm is used to minimize the SSE by approximating the optimum ARMAparameters. Several methods such as steepest descent, gradient search, Newton, and Levenberg–Marquardt can be usedwithin the RLS to update the model parameters.

As previously indicated, identification techniques depend on two main choices: model structure and algorithm. Theadvantage of the ARMA structure is that it provides the transfer function parameters which is essential to the proposed strat-egy. Other structures such as frequency analysis or neural network can identify the system but cannot provide the results ina transfer function format. As for the algorithm, the RLS was used because it can have accurate results with good convergenceproperties. These RLS features are well established in the literature [9,34]. This feature is important in this work as the sys-tem is identified in real time.

3.2. Induction motor control

The development of semiconductor power conversion technology has led to a widespread use of electric drive with induc-tion motors, and new control systems for these motors.

There are certain limitations in the use of a control method for induction motors control system. It is difficult to identifycommon approaches to the synthesis of induction motors control system. The control of induction motors is complicatedsince it involves several interacted variables. The most significant of which are as follows:

(1) The electromagnetic torque of the induction motor is determined by the product of the two resulting vectors of theelectromagnetic parameters of the stator and the rotor and therefore a function of four variables.

(2) There is a strong interaction magnetizing forces of the stator and the rotor, the mutual state of which varies contin-uously with the rotation of the rotor.

Fig. 4. Photograph of AC motor experiment set up.


Induction motors, together with controlled power converters are complex and nonlinear control systems [19]. Fullmathematical description of such systems is quite cumbersome and hard to apply to practical control systems. However,the design of induction motors control systems is a proliferation of simple methods based on the principle of cascade con-trol. The use of these techniques allows reasonable opportunity to simplify the mathematical description of the controlsystems.

In an adjustable AC drive system, for speed control, the induction motor normally requires variable-voltage variable-fre-quency power supply (voltage source inverter) [20,21], or variable-current variable-frequency power supply (current sourceinverter) [22,23].

The voltages and currents are described by complex two-dimensional vectors. The mathematical treatment is different inthe case of voltage control and current control of the induction motor. Current control is normally preferred. However, thereare some problems associated with current control [24] and are summarized below:

(1) Since the stator equation is eliminated, the control system is dependent on the rotor equation, and thus is criticallydependant on the rotor inductive time constant. This time constant varies with rotor resistance, which varies withrotor temperature, and it is difficult to measure the rotor temperature. A more serious problem occurs when the rotoriron goes into magnetic saturation and the rotor time constant collapses. Then the whole control system may collapse.

(2) There is a nonlinear relationship between current and magnetic field strength. This nonlinearity is a source of torqueripple.

FilterSignal

Generator

OutputPort

Input Port

ARMA Model

SIMULATION ENVIRONMENT

REAL SYSTEM

DAQ INTERFACE

TACHOMETER

AC MOTORINVERTER

PWM

RLS Algorithm

Fig. 5. Block diagram for on-line system identification based on HIL.


A simple physical law proves that the induction motor should be voltage controlled instead of current controlled [25].Voltage control automatically eliminates the effect of the nonlinear iron magnetization curve. Only a small correction termhas to be modulated according to the stator voltage drop caused by the nonlinear magnetization current. This correctionterm is most important at low speed operation of the motor.

3.3. Proposed strategy based on Hardware-In-the-Loop

The HIL is an environment where software components within a simulation program are worked in conjunction withreal system components implementing an integrated system. This setting gives the capability of monitoring the system’sbehavior by observing its response using virtual monitoring components [31,32]. Usually, HIL is used for testing

0 0.5 1 1.5 2 2.5 3

0

0.2

0.4

0.6

Time (secs)

Actual Output (Red Line) vs. The Predicted Predicted Model output (Blue Line)

0 0.5 1 1.5 2 2.5 3

-0.06

-0.04

-0.02

0

0.02

0.04

Time (secs)

Error In Predicted Model

Spee

d (v

)er

ror

0 0.5 1 1.5 2 2.5 3

0

0.2

0.4

Time (secs)

Actual Output (Red Line) vs. The Predicted Predicted Model output (Blue Line)

0 0.5 1 1.5 2 2.5 3-0.02

0

0.02

0.04

Time (secs)

Error In PredictedModel

Spee

d (v

)er

ror

a

b

Fig. 6. Impulse response of real system compared to the identified model output for (a) 6th order model and (b) 22nd order model.

0 20 40 60 80 100 120 140 160 180 2000

0.5

1

1.5

2

2.5

3

3.5

Time

Spe

ed (

v)

6th order model

Real System

Fig. 7. Step response of the real system vs. the identified 6th order model.

0 50 100 150 200 250 300-0.5

0

0.5

1

1.5

2

2.5

3

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Spee

d (v

)

6th order

Math model

Real System

0 100 200 300 400 500 6000

0.5

1

1.5

2

2.5

3

Time

Spee

d (v

)

6th order

Math model

Real System

a b

Fig. 8. Comparison of real system’s, mathematical model’s, and identified model’s responses to (a) trapezoidal input and (b) multi-trapezoidal input.


hardware controllers on software models. However, in this research, software components are used to identify physicalsystems.

The strategy adopted in this research was divided into three successive stages: on-line identification, off-line controllerdesign, and on-line control. First, the induction motor system was connected to the computer through DAQ where an RLSalgorithm was used to identify the transfer function on-line. Next, the controller design was performed in a pure simulationenvironment using the identified transfer function from the first step. Once the controller design was completed, it was ap-plied to the motor to perform on-line control. All stages were applied in MATLAB/Simulink environment.

The advantages of the proposed method include the following: accuracy in the identification and system control, optimiz-ing time resources and minimizing the cost as a result of on-line identification and off-line controller design, increased flex-ibility of the controller, and user friendly.

The second step of the proposed strategy, which is the off-line controller design, offers the following improvements overtraditional design strategies:

1. The induction motor to be controlled will not be used during the experimentation of the controller design and parametertuning and therefore the down time of the induction motor will be minimized. This might be a crucial time saving issuewhen the motor is used production line. Equally important, damage to the motor due to inappropriate parameter valuesis avoided.

2. The off-line tuning gives much more flexibility to the designer in order to design and test many controllers in a simulationenvironment. This will result in obtaining an optimized controller in minimum time.


4. Results and analysis

The 3-stage proposed strategy was implemented and tested in the Laboratory. This section provides the details of thestrategy implementation.

The experiment setup used a P4, 3 GHz desktop computer with a National Instruments Data Acquisition (DAQ) card6036E that has a sampling rate of 200 KS/s. The DAQ card had 16 analog inputs, 2 analog outputs, and input voltage rangeof ±10 V. The induction motor system was composed of a motor, pulse width modulator, inverter, and tachometer. Theinduction motor is a three-phase squirrel gage with the following ratings: nominal power 300 W, nominal speed2870 rpm, nominal torque 1 N m, nominal line voltage 380 V, nominal frequency 50 Hz. The output from the tachometerwas 0.9 mv/rpm. The experimental test rig photograph is depicted in Fig. 4.

In this setup, one input and one output signals were used. The output was generated from the computer and sent from theDAQ via D/A channel to the motor system. While the input signal was read from the tachometer via the A/D channel. Bothsignals were used in a MATLAB/Simulink environment where the software algorithm was implemented. Real time windowstarget (rtwt) toolbox within MATLAB was utilized for real time system interface.

4.1. Stage one: system identification

Fig. 5 shows the block diagram for the adopted on-line system identification stage. One way to implement the systemidentification is to use an ideal impulse as the excitation signal. However, for implementation purposes a pulse is used in-stead. The period of the pulse should be short enough to represent an ideal impulse but also long enough to activate themotor to its desired settling speed. Several tests and experiments were conducted to choose the pulse width value to be0.15 s. A pulse with 5 V amplitude was applied from the computer to the induction motor system. Measured tachometeroutput was sampled at 0.05 s and combined with the input signal to formulate patterns. These patterns were used in aRLS algorithm to establish the estimated system transfer function via ARMA models. The sampling rate was selected in orderto obtain appropriate number of samples (e.g. 300 samples) to be used for the RLS algorithm.

The choice of ARMA model order affects convergence and therefore several orders were simulated in order to choose themost appropriate. Two of those model orders, 6th and 22nd, are presented in Fig. 6 where motor response vs. identified

Table 2statistical data for the real, the identified model, and the mathematical model responses.

Input signal Statistical criteria

System Min Max Mean Standard deviation

Trapezoidal Real �0.0473 2.884 1.446 1.235Identified Model �0.01737 2.858 1.447 1.282Mathematical Model 0 2.856 1.529 1.139

Multi-trapezoidal Real �0.05157 2.889 1.891 1.126Identified Model �0.01737 2.858 1.891 1.163Mathematical Model 0 2.856 1.916 1.083

Fig. 9. Identified model controlled response off-line with PID controller (P = 3.68, I = 0.02, D = 0).


model responses to the pulse input are provided. Also, the errors between the motor and model responses are provided inthe same figure. Both order models gave a good approximation of the original motor response. Although the 22nd ordermodel resulted in a slightly smaller error, the 6th order model was used throughout this paper because lower order modelsconverge faster.

The identified model was further tested with a step input. The model response was compared to the real system as shownin Fig. 7. It is observed that the identified model had a very close behavior to the real system in terms of rise time, overshoot,and settling error.

Furthermore, trapezoidal and multi-trapezoidal inputs were applied to the identified model, real system, and mathemat-ical model. Results are presented in Fig. 8. Again, results showed that the 6th order identified model used does very well inapproximating the real system behavior. In fact, the identified model did better than the mathematical model. These resultsare presented in Table 2. Comparison among the three responses showed the mean and standard deviations of the identifiedmodel is almost equal to the real system. Therefore, the model was considered appropriate to be utilized in control systemdesign via HIL.

Speed Profile

OutputPort

Input Port

SIMULATION ENVIRONMENT

REAL SYSTEM

DAQ INTERFACE

TACHOMETER

AC MOTORINVERTER

PWM

Controller

-+

Fig. 10. Block diagram for on-line control via HIL.

Fig. 11. Simulink block diagram for on-line control.

Fig. 12. Real system speed response with on-line PID controller (P = 3.68, I = 0.02, D = 0).


4.2. Stage two: simulation control

The 6th order model obtained from the identification stage was used to replace the real system within Simulink in orderto design the suitable controller. At this stage, the designer can utilize any control method that is appropriate on the iden-tified model and tests the results in a simulated environment. The scope of this paper is concerned with the strategy de-scribed in Section 3 rather than selecting among different control methodologies.

In this paper, a controller based on Proportional–Integral–Derivative (PID) [33] was applied due to its simplicity and widepractice in the industry. A trapezoidal speed profile was given as the desired response. The PID tuning was applied usingZiegler–Nichols tuning method. The final PID parameters were set to P = 3.68, I = 0.02, and D = 0. The result is presentedin Fig. 9 which shows that the controller was successful in forcing the model to follow the desired speed.

4.3. Stage three: on-line control

Once the controller was optimized on the identified model in a simulated environment, the identified model was thenreplaced by the real system for the final controller test. In this stage, the designed controller was applied to the real systemthrough Real time windows target within Simulink according to the experimental setup explained in Section 4. Fig. 10 showsthe block diagram for the on-line control. Note that this setup differs from the system identification setup, Fig. 5, in that itapplies a controller block instead of the ARMA/RLS block.

Fig. 11 shows the Simulink/software program used to run the on-line controller experiment. Note here that the PID con-troller is based in the PC software. The ‘‘Real System” in Fig. 11 is a Simulink block that is interfaced with the DAQ. The DAQin turn acquires the I/O data from the real system, induction motor, and forwards it to this block.

Fig. 13. Real system speed response to a multi-trapezoidal input with on-line PID controller (P = 3.68, I = 0.02, D = 0).


For the final step, speed profiles that are similar to the profiles used in stage two are applied. The speed response of thereal system is shown in Fig. 12 for the trapezoidal and Fig. 13 for the multi-trapezoidal reference profiles. The real system,induction motor, was able to follow the desired reference signals with minimum error. This means that the designed con-troller in stage two performed well on the real system. It is worth noting that further fine tuning can be applied at this stage.

The effects of parameters, such as temperature, on the induction motor model are established in the literature [28]. Theidentified model used in this paper establishes a reference model which is used for off-line controller initial design. The thirdstep in the proposed strategy, the on-line control, will compensate for the parameter variation through on-line fine tuning.Therefore the performance of the identified model is adequate for our purpose within our strategy.

5. Conclusions

In this paper, a strategy to identify and control an induction motor is provided. The proposed strategy is composed ofthree stages. In the first stage, ARMA models were used to identify the motor transfer function. In the second stage, the iden-tified model was used in a simulation environment in order to design the controller off-line. In the last stage, the virtual con-troller was applied to the induction motor via HIL environment.

In the identification stage, an impulse was applied to the motor and the speed was measured. Patterns of measured in-put–output signals were used in a software environment to identify the transfer function by optimizing its parametersthrough an RLS algorithm. In the control stages, the identified model was adopted within a simulation environment to testand optimize appropriate controllers. These controllers were then applied to the real system.

Experimental results using PC/DAQ and MATLAB/Simulink were carried out. Results showed that the proposed strategywas able to identify and control the motor behavior for different speed profiles.

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Hardware-In-the-Loop for on-line identification and control of three-phase squirrel cage induction motorsIntroductionModeling and simulation of three-phase squirrel cage induction motorSystem identification and control through HILSystem identificationInduction motor controlProposed strategy based on Hardware-In-the-Loop

Results and analysisStage one: system identificationStage two: simulation controlStage three: on-line control

ConclusionsReferences

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