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Performance Analysis of Field Oriented Induction Motor using Fuzzy PI and

Fuzzy Logic based Model Reference Adaptive Control

Article  in  International Journal of Computer Applications · March 2011

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 International Journal of Computer Applications (0975 – 8887)
Volume 17– No.4, March 2011

Performance Analysis of Field Oriented Induction Motor

using Fuzzy PI and Fuzzy Logic based Model Reference
Adaptive Control

Bharat Bhushan Madhusudan Singh Prem Prakash

Electrical Engineering Electrical Engineering Electrical Engineering
Department Department Department
Delhi Technological University Delhi Technological University Delhi Technological University
Delhi, INDIA Delhi. INDIA Delhi, INDIA

ABSTRACT method has a simple control structure [1] and is implemented

This paper presents the dynamic performances of an indirect easily, for general-purpose industrial applications. However to
vector controlled induction motor (IVCIM) using a fuzzy improve speed control performance of the scalar control
logic (FL) based model reference adaptive control (MRAC) method, an encoder or speed tachometer is required; which is
slip gain tuner for speed regulation in the drive. In high an expensive and less reliable solution. For achieving variable
performance AC drives the motor speed should closely match speed operation, the frequency control method [2] of the cage
with the specified reference speed irrespective of the motor is the best method among all the methods of the speed
variations in the load, motor parameters and model control. There is a wide variety of applications such as
uncertainties. Two fuzzy controllers combined with MRAC machine tools, elevators; mill drives etc. where quick control
reactive power and stator direct axis (d-axis) voltage estimator over the torque of the motor is essential. Such applications are
have been used to tune the slip gain of the IVCIM drive dominated by DC drives and cannot be satisfactorily operated
against parameter variations and model uncertainties. An by an induction motor drive with constant volt/hertz scheme.
integrated mathematical model of the control scheme has been DC motors are easily controllable than AC motors but they
developed and simulated in MATLAB for Indirect vector are costly and less efficient.With the vector control or field
control of an Induction motor. The simulated performances of oriented control theory [4] induction motors can be controlled
the FL-MRAC slip gain tuner based IVCIM drive is compared like a separately excited DC motor. But implementation of
to fuzzy PI controller. The simulated results in different vector control requires online computational capability which
dynamic operating conditions such as sudden change in is achieved using micro controller Digital signal processor. A
command speed, step change in load, etc are demonstrated Fuzzy Learning Enhanced speed control [5] of an indirect
through necessary waveforms. The comparison of simulated field oriented induction motor drive is proposed such that the
results show that the fuzzy logic MRAC slip gain tuner based machine can follow a reference model to achieve desired
IVCIM drive is more robust and effective in minimizing the speed performance. The analysis and design of the fuzzy logic
detuning effect in the drive due to parameter variations and controller an indirect vector controlled induction motor drive
model uncertainties. is investigated. The integral of time [6] by absolute error
criterion is then used to evaluate the performance of the fuzzy
Keywords speed controller for different operating conditions. Influence
Fuzzy logic; PI controller; field oriented induction motor; of the rotor resistance deviation to the system performance is
model reference adaptive control studied and a rotor resistance estimator [7] using the fuzzy
logic principles is described. A novel speed control scheme
[8] of an induction motor using fuzzy logic control is
described. A model reference adaptive scheme [10] is
1. INTRODUCTION
proposed in which an adaptation mechanism is executed using
Induction motor has a simple and rugged construction. a PI controller and a fuzzy logic. The performance of a FLC
However the speed control of the induction motors are not [11] has been investigated and compared to that of the results
simple difficulties due to its complex and nonlinear obtained from the conventional PI controller based drive at
mathematical model which involves parameters that vary with different operating conditions such as sudden change in load.
temperature, frequency and other operating conditions. The The simulation results demonstrate that the performance of
variations of parameters have significant effect on the the FLC is better than that for the conventional controller.
accuracy of control speed and torque and other operating Speed regulation of the field oriented induction motor is
performance of the motor. It is therefore essential to optimize achieved by using a fuzzy model reference learning controller,
the motion control performance by designing intelligent [12] which is more robust and does not require rigorous
adaptive controller based on fuzzy logic, neural network and tuning because of its adaptive nature as compared to the
expert systems, so that torque and flux have dynamic ideal proportional integral and direct fuzzy controllers. A new
response in high performance AC drives. Scalar control design of fuzzy logic controller [13] with fuzzy adapted gains

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 International Journal of Computer Applications (0975 – 8887)
Volume 17– No.4, March 2011

for speed regulation of an indirect field oriented induction 2. MATHEMATICAL MODEL OF

motor is presented. A proportional-integral and fuzzy logic
INDIRECT VECTOR CONTROL OF
speed controllers operating in indirect field orientation [14]
are designed and compared under no load and various load
INDUCTION MOTOR
conditions with different reference speeds. An adaptive neuro-
Fig.1 shows a MATLAB/SIMULINK model of indirect vector
fuzzy inference system (ANFIS) based intelligent control [15]
control induction motor. A 3-phase, 50 hp, 460V, 50 Hz
of vector controlled induction motor drive is proposed. The
induction motor is supplied through a current-controlled
proposed neuro-fuzzy speed controller incorporates fuzzy
voltage source inverter (CC-VSI), realized through an
logic algorithm with a five-layer artificial neural network
universal bridge. The gate drives for universal bridge are
structure. A vector control structure [16] combining the
generated by PWM current regulator. The controller makes
advantages of two types of field oriented procedure is
two stage of inverse transformation, as shown so that control
proposed for a squirrel cage induction motor supplied from a
current id*, iq* correspond to machine current id, iq,
voltage source inverter (VSI). Approach with reference model
respectively, in addition the unit vector assures correct
has [17] been chosen in terms of tracking, and disturbance
alignment of id current with the flux vector ψr and iq
rejection with high robustness. Two speed control techniques,
perpendicular to it . The ds-qs axes are fixed on the stator, but
[18] scalar control and indirect field oriented control are used
the dr-qr axes are fixed on rotor, moving at speed ωr.
to compare the performance of the control system with fuzzy
Synchronously rotating axes de-qe is rotating ahead of the dr-qr
logic controller [19-20]. Over the last two decades the
axes by the positive slip angle θsl corresponding to slip
principle of vector control of AC machines has evolved,
frequency ωsl. Since the rotor pole is directed on the de axis
manifold and an induction motor can be controlled to give
and ωe = ωr+ωsl. The unit vector signal is determined as:
dynamic performance comparable to what is achievable in a
separately excited DC drive. M. Nasir Uddin et al. [21]
(1)
pointed out the condition in which use of fuzzy logic
controller with indirect vector control induction motor drive is It is to be noted that the rotor pole position is not absolute, but
more effective as compared to conventional linear controllers. is slipping with respect to the rotor at frequency ωsl .
The motor-control issues are traditionally handled by fixed-
gain proportional-integral (PI) and proportional-integral- For decoupling control, the stator flux component of current id
derivative (PID) controllers. However, the fixed-gain should be aligned on the de axis, and the torque component of
controllers are very sensitive to parameter variations, load current iq should be on the qe. Control equation of indirect
disturbances, etc. Thus, the controller parameters have to be vector control can be written as (P.Vas and J.Li, 1993, B.K
continually adapted or tuned. The problem can be solved by Bose, 2002):
several adaptive control techniques such as model reference
adaptive control (MRAC), sliding-mode control (SMC) (2)
variable structure control (VSC), and self-tuning PI
controllers, etc. The design of such controllers depends on the
(3)
exact system mathematical model. However, it is often
difficult to develop an accurate system mathematical model
due to unknown load variation, unknown and unavoidable The rotor flux linkage equation written as:
parameter variations due to saturation, temperature variations,
and system disturbances. In order to overcome the above (4)
problems, recently, the fuzzy-logic controller (FLC) [21-24]
with power-full estimation technique is being used for motor
control purpose. In this paper PI, fuzzy and model reference (5)
adaptive control techniques have been implemented on field
Therefore, the rotor d-q currents are :
oriented induction motor for speed regulation. A comparative
analysis of three controllers has been shown for IVCIM in
term of motor currents, speed and torque. The main (6)
advantage of fuzzy logic control method as compared to
conventional control techniques resides in fact that no exact (7)
mathematical modeling is required for controller design and
also it does not suffer from the stability problem. This Paper is
organized in six sections 2 gives modeling of field oriented The rotor currents can be further written in the form of rotor
induction motor. Section 3 presents fuzzy logic and fuzzy flux linkages:
logic model reference adaptive control. Section 4 presents the
simulation results and discussion while section 5 gives
conclusion followed by references in section 6.

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 International Journal of Computer Applications (0975 – 8887)
Volume 17– No.4, March 2011

Phir Id

flux calculation
1
z

0.96 Phir* Id*

Theta Id
Phir * id * Calculation
Iq
Iabc Iq
Phir Teta
ABC-DQ
wm

theta calculation

Id * 25
1 Te*
Input Iq * Iq *Iabc* Iabc* load torqe
Phir Pulses
Teta
Iabc 1
iqs calculation
DQ-ABC Stator Currents
Current Regulator
Tm
g is_abc
2
+ A Rotor Speed
A m m wm
B
B
Te 3
DC Voltage Source - C
C Torque
Induction Motor Machine
Universal Bridge
50 HP / 460 V, 60 HzMeasurements
1
z

1
z

Fig.1 MATLAB model of indirect vector control induction Motor Drive

(8) Therefore, the rotor flux is directly proportional to current i ds

in steady state.

(9) 3. FUZZY LOGIC AND FUZZY LOGIC

BASED MODEL REFERENCE
where, ADAPTIVE CONTROL
3.1 Fuzzy Logic: Fuzzy logic [25-26] implementation
For decoupling control requires no exact knowledge of a system model. Fuzzy
logic applications are being studied throughout the
(10) world by control engineers. The result of these studies
has shown that fuzzy logic is indeed a powerful control
(11) tool, when it comes to control system or process. The
Fuzzy Logic Toolbox in MATLAB has several features
which allow to create and edit fuzzy inference systems.
Also the total rotor flux is directed on de axis :
One can create these systems using graphical tools or
command-line functions, or one can generate them
(12) automatically using either clustering Fuzzy techniques.
There are five primary GUI tools for building, editing,
and observing fuzzy inference systems in the Fuzzy
(13)
Logic Toolbox
where , The Fuzzy Inference System or FIS Editor.
The Membership Function Editor.
if rotor flux = constant ,which is usually the case equation . The Rule Editor.
(14) The Rule Viewer.
The Surface Viewer.

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 International Journal of Computer Applications (0975 – 8887)
Volume 17– No.4, March 2011

d/dt CE
∆Ks
∆Ks
i*ds Reference ∆Q(pu) computat KS
Σ
i*qs model Q* 1/Qb X
E ion
+ +
ωe
- +
Vsds
Vsqs Actual
Kf Rule Fuzzy
isds estimation
base І І sets І І
isqs Q

FLC - 2
isds
isqs Reference
ωe model V*ds 1/Vb X
+
- Rule
Vsds (1-Kf ) base І
Actual Kf
Vsqs estimation Vds computat
1 + ion
- Fuzzy
sets І
Kf
Cosθe Sinθe
I*qs ωe
FLC - 1

Fig.2 Model Reference Adaptive Control with Slip Gain Tuner

objective is to provide an adaptive feedback control for

The MATLAB/ simulink machine implementation of a fast convergence at any operating point ,irrespective of
FLC involves the use of the concept of fuzzy subset, the strength of error signal E and its derivative signal
membership function and rule based modeling. CE.

3.2 Fuzzy Logic Based Model Reference 3.3 Relationship of Reactive Power (Q*)
Adaptive Control with Slip Gain Tuner and D-Axis Voltage (Vds*) in MRAC
for IVCIM Drive: The MRAC method based Slip Gain Tuner
on reactive power and stator d-axis voltage are
From the de-qe model of IM, the stator equations
combined together with a weighting factor which is
are
generated by a fuzzy controller. The weighting factor
ensures the dominant use of reactive power method in
low speed high torque region whereas the d - axis (15)
voltage method is dominant in high speed low torque
region (Gilberto C.D. Sousa et al.1993). A second (16)
fuzzy controller tunes the slip gain based on combined
detuning error and its slope so as to ensure fast
convergence at any operating point on torque-speed At steady state condition under vector control,
plane. The rule base matrix for the fuzzy logic
controller generating detuning factor (Kf) is given in (17)
Table 2: It clearly shows that if speed is low (L) and
torque is high(H) then weighting factor is high(H). A (18)
block diagram of fuzzy logic based on model reference
adaptive control (MRAC) of slip gain tuner is shown in
Fig.3. Here the reference model output signals X* that (19)
satisfied the tuned vector control is usually a function
of command current ids*, iqs*, machine inductance, and (20)
operating frequency. The adaptive model X is usually
estimated by machine feedback voltage and current.
The reference model output is compared with that of
adaptive model and the resulting error generates the
(21)
estimated slip gain through a fuzzy P-I controller.
The slip tuning occurs when X matches with X*.The (22)

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 International Journal of Computer Applications (0975 – 8887)
Volume 17– No.4, March 2011

(reference) (23) μ (du)

NL NM NS NVS Z PVS PS PM PL

(actual) (24)

(actual) (25)
-1 -0.8 -0.6-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 du(pu)
(reference) (26)
Fig. 5 Membership function for Output

where cos θe and sin θe are the unit vector components.

The loop errors are divided by the respective scaling
factor to derive the per unit variable ΔQ and the Δvds Table 1 Rule Based Matrix for fuzzy controller
for manipulation by fuzzy controller. The combined
error signal for fuzzy PI controller (FLC 2) is given as:
CE/E NL NM NS Z PS PM PL
(27)

PL Z PS PM PL PL PL PL
Fuzzy controller FLC 2 generates the corrective
incremental slip gain ΔKs based on the combined
detuning error E and its derivative CE as shown in PM NS Z PS PM PL PL PL
figure 3 and 4. Membership function for output
variable is shown in figure 5.
PS NM NS Z PS PS PL PL
μ (e)

NL NM NS Z PS PM
Z NL NM NS Z PM PM PL
PL
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
NS NL NL NM NS Z PS PM
PM PL

NM NL NL NL NM NS Z PS

e(pu)
. -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 NL NL NL NL NL NM NS Z

Fig. 3 Membership function for error

μ (ce)
Table 2 Rule base matrix for weighting factor (Kf)
NL NM NS Z PS PM
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Iqs\ωe H L
PL
PM PL

H M H
ce(pu)
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

L L M
Fig. 4 Membership function for change in error

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 International Journal of Computer Applications (0975 – 8887)
Volume 17– No.4, March 2011

4. RESULTS AND DISCUSSIONS

4.1 Performances of IVCIM Drive using

Fuzzy-PI Control
Fig.6 shows the performance characteristic of a 50 hp, 460 V,
60 Hz IM, operating at no load with a fuzzy PI speed
controller. The reference speed is 120 rad/sec. it is observed
that motor pick up the reference speed at t = 0.6 sec Fig.7
shows the performance characteristic of motor, when a sudden
change in reference speed from 120 to 160 rad/sec is made at t
= 0.2 sec. it is observed from the waveform of the motor that Fig.7 Response of FLIVC with a step change in reference
speed (120 to 160 rad/sec) at t = 0.2 sec
motor speed tracks the change in reference speed quickly and
steady state and there is no significant offset. This is due to
the facts that the fuzzy control is a nonlinear control and the
IM motor mathematical model is also non-linear and
complex. Fig.8 shows the response of the fuzzy logic IVCIM
with sudden change in load torque at t = 0.2 sec. the motor
torque rise quickly to 200 N-m. The speed of the motor dips
momentarily, FLC current regulator is able to maintain
specified speed 120 rad/sec.

Fig.8 Response of FLIVC with a step change in load(0 to

200 rad/sec) at t = 0.2 sec.

4.2 Performance of Indirect Vector

Control IM Using Fuzzy Logic Based
MRAC Slip Gain Tuner
A FL based MRAC slip gain tuner is designed and used as a
speed controller for a 50 HP, 460V, 4 poles squirrel cage
induction motor. The simulated performances of IVCIM drive
without and with MRAC slip gain tuner with variation in rotor
resistance are shown in figures 9 and 10 respectively. It is
observed that the MRAC based FLC controller provides a
faster dynamic response of induction motor and also sensitive
to variation in rotor resistance. The induction motor with
Fig.6 Performance of Fuzzy PI indirect vector control
(FLIVC) at no load with reference speed 120 rad/sec conventional FLC-PI controller requires approximately 1.2
sec. to demanded speed of 120 rad/sec, while MRAC control
needed 0.9 sec. achieve the reference speed.

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 International Journal of Computer Applications (0975 – 8887)
Volume 17– No.4, March 2011

Fig.11 shows the performance characteristics of IVICM with

fuzzy PI control and fuzzy logic based MRAC tuner, when the
rotor resistance is changed from 0.228 Ω to 0.171 Ω, (25%
decreases in rotor resistance). It is observed that the torque
decreases from 300 to 210 N-m with fuzzy PI control while
performance of fuzzy logic based MRAC tuner maintains the
torque 300 N-m, even though the rotor resistance decreases by
25% and makes the response independent of rotor resistance
variations. Comparison of torque developed by motor with
fuzzy PI controller based IVCIM drive and fuzzy logic based
MRAC slip gain tuner based IVCIM drive during variation of
rotor resistance shows that due to variation of rotor resistance,
torque decreases to 200 N-m in fuzzy PI control of IVCIM,
while it is maintained at 300 N-m by fuzzy logic MRAC
based tuner. The comparison of simulated results show that
Fig.10 Performance of indirect vector control using Fuzzy
the fuzzy logic MRAC slip gain tuner based IVCIM drive is
logic based MRAC slip gain tuner at R’ =0 .75 R
more robust and effective in minimizing the detuning effect in
the drive due to parameter variations and model uncertainties

Fig.11 Torque response comparisons between FLC based

IVC and fuzzy logic MRAC based IV

5. CONCLUSION
This paper presents a comparative performance study of
IVCIM drive with Fuzzy-PI and FL based MRAC slip gain
tuner for speed control. A FL control in IVCIM gives superior
performance in terms of fast dynamic responses and stiffer
speed regulation. However, a conventional FL controller is
Fig.9 Performance of indirect vector control using Fuzzy not capable in maintaining the performance of the motor due
logic P-I controller at R’ = 0.75R rotor to parametric variations. A FL based MRAC slip gain
tuner provides an effective means in speed and torque control
of the IVCIM drive even with variation rotor resistance
operation of motor.

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 International Journal of Computer Applications (0975 – 8887)
Volume 17– No.4, March 2011

6. REFERENCES Field Oriented Induction Machine Drives,” IEEE Int.

[1] P. Vas, Vector control of AC machines, New York: Conf, pp. 397-400, 2007.
Clarendon, 1990. [15] R. A. Gupta, R. Kumar and R. S. Surjuse, “ANFIS
[2] B.K. Bose, power electronics and AC Drive. Englewood Based Intelligent Control of Vector Controlled Induction
Cliffs, NJ. Prentice hall, 1986 Motor Drive,” Int. Conf on Emerging Trends in
Engineering and Tech., pp. 674-680, 2009.
[3] N. Mariun, S. B Mohd Noor, J. Jasni, and O. S.
Bennanes, “A Fuzzy Logic Based Controller For An [16] M. Imecs, I. I. Incze and C. Szabo, “Dual Field
Indirect Vector Controlled Three-Phase Induction Orientation for Vector Controlled Cage Induction
Motor” IEEE IECON Conf. Rec., Vol. 4, pp. Motors,” IEEE Int. conf, pp. 143-148, 2009.
1-4, Nov. 2004. [17] A. Mechernene, M.Zerikat and S. Chekroun, “Indirect
[4] F. Biaschke, “ The principle of field oriention as applied Field Oriented Adaptive Control of Induction Motor
to new transvector closed loop control system for Based On Neuro-Fuzzy Controller,” Mediterranean conf.
rotating field machine,” Siemens Rev, vol. 34,,pp 217- on Control & Automation, Morocco, pp. 1109-1114,
220‟may 1972 2010.

[5] L. Zhen and L. Xu, “Fuzzy Learning Enhanced Speed [18] A. M. Eltamaly, A. I. Alolah and B.M. Badr, “ Fuzzy
Control of an Indirect Field Oriented Induction Machine Controller for Three Phases Induction Motor Drives,”
Drive,” proc. of IEEE Int symposium on Intelligent IEEE Int. Conf, 2010.
Control, Dearborn, MI, pp.109-114, Sept, 1996. [19] Vinod Kumar and R. R. Joshi, “Hybrid Controller based
[6] M. T. Cao, J.L. S. Neto and H. L. Huy, “Fuzzy Logic Intelligent Speed Control of Induction Motor”, Journal
Based Controller for Induction Motor Drives,” proc. of of Theoretical and Applied Information Technology, pp.
IEEE Int. conf. , pp.631-634, 1996. 71-75, 2005.

[7] M.T.Cao and H.L. Huy, “Rotor Resistance Estimation [20] CHE-MUN ONG,”Dynamic simulation of Electrical
using Fuzzy Logic for High Performance Induction Machaniry using MATLAB / Simulink”, Printic hall
Motor Drives,” Proc. of IEEE Int. conf , pp.303- 308, PTR in 1998.
1998. [21] M M. N. Uddin, T. S. Radwan, and M. A. Rahman,
[8] M. N. Uddin, T. S. Radwan and M. A. Rahman, “Performances of Fuzzy-Logic-Based Indirect Vector
“Performances of Novel Fuzzy Logic Based Indirect Control for Induction Motor Drive”, IEEE Transactions
Vector Control for Induction Motor Drive,” Proc. of On Industry Applications, Vol. 38, No. 5, pp. 1219-1225,
IEEE Int. conf, pp. 1225-1231, 2000. Sept./Oct. 2002.

[9] L. Zhen and L. Xu, “Fuzzy Learning Enhanced Speed [22] L.A. Zadeh,”fuzzy theory,” university of California,
Control of an Indirect Field Oriented Induction Machine Berkely,1965.
Drive,” IEEE Trans on Control Systems Technology, [23] Li Zhen and Longya Xu, “On-Line Fuzzy Tuning of
vol. 8, No. 2, pp. 270-278, March 2000. Indirect Field-Oriented an Induction Machine Drives”,
[10] B. Karanayil, M. F. Rahman and C. Grantham, “PI and IEEE Transactions on Power Electronics, Vol. 13, No. 1,
Fuzzy Estimators for On-line tracking of Rotor pp. 134-138, January 1998.
Resistance of Indirect Vector Controlled Induction Motor [24] J.-S.R.Jang, “Fuzzy Controller Design Without Domain
drive,” Proc. of IEEE Int. conf, pp. 820-825, 2001. Experts”, IEEE International Conference on Fuzzy
[11] N. Mariun, S.B.M. Noor, J. Jasni and O. S. Bennanes, “A Systems, 8-12 March 1992 pp. 289 – 296.
Fuzzy Logic Based Controller for an Indirect Vector [25] P. Vas, and J. Li, 1993, „Simulation Package for Vector
Controlled Three Phase Induction Motor,” Proc. of IEEE
Int. conf, pp.1-4, 2004. Controlled Induction Motor Drives‟, Oxford University
press.
[12] H.U. Rehman and W. Mahmood, “A Fuzzy Model
Reference Learning Controller Based Direct Field [26] Bimal K. Bose,2002,‟Modern Power Electronics and AC
Oriented Control of Induction Machine,” IEEE Int. conf,
Drives‟, Pearson Education Asia.
2006.
[13] K. Kouzi, L. Mokrani and M. S. N. Said, “A New Design
of Fuzzy Logic Controller with Fuzzy Adapted Gains
Based on Indirect Vector Control for Induction Motor,”
IEEE Int. conf, pp. 362-366, 2003.

[14] M. Masiala, B. Vafakhah, A. Knight and J. Salmon,

“Performances of PI and Fuzzy-Logic Speed Control of

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