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A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
A Comparative Analysis Of PI And Neuro Fuzzy
Controllers In Direct Torque Control Of Induction Motor
Drives
A.Sudhakar*,M.Vijaya Kumar**
*(Department of Electrical and Electronics Engineering,S.V.I.T Engg College, Anantapur,India)
**(Department of Electrical Engineering,J.N.T.U,Anantapur,India.)

Abstract:
The implementation of conventional harmonic currents.
DTC in Induction Motor drives consisting of Various methods have been proposed to
PI torque controller suffers from complex overcome these drawbacks, such as variable
tuning and Overshoot problems. One of the structure control approach,fuzzy logic
various methods to tackle this problem is control,neural netwok control,adaptive control
implementation of intelligent controllers like methods have been proposed for motion control
neuro fuzzy-based controller This paper of Induction motor drive[2-12]. The PI control
presents the simulation and analysis of a is simple and offers a wide stability margin, but
Neuro fuzzy-based torque controller for DTC it incorporates tuning,overshoot problems. The
of Induction motor drives. This control fuzzy logic can compensate the system
scheme uses the speed error calculated from nonlinearities through human expertise. Yet, it
reference speed and estimated speed which relies too much on the intuition and experience
generates the estimated Torque and of the designer. The neural network can handle
compared with the actual Torque and the complicated nonlinear characteristics of the
generates the inverter switching states. In this system, but suffer from the problem of lengthy
paper a modified ANFIS structure is training and convergence time. The adaptive
proposed. This structure generates the desired control can self adjust the controller parameters
reference voltage which regulates the to adapt system parameter variations.
performance of induction motor. Unfortunately, it generally requires a reference
Comparisons and analysis under various model of the system.
operating conditions between hysteresis-based
PI torque controller and Neuro fuzzy-based The controller proposed in this paper is
torque controller are presented. The results neuro fuzzy-based controller which gives better
show that the proposed controller managed to performance compared to PI controller. The
reduce the overshoot and give better neural network is well known for its learning
performance. ability and approximation to any arbitrary
continuous function.[9] It has been proposed in
Keywords-direct torque control, induction the literature that neural networks can be applied
motor drive, PI controller, complex tuning, to parameter identification and state estimation .
Overshoot, neuro fuzzy controller. The fuzzy logic controller solves the problem of
non-linearities and parameter variations of IM
1 Introduction drive. It achieves high dynamic performance and
Direct torque control(DTC) of induction accurate speed control with good steady-state
motor drives has gained popularity due to its characteristics. The fuzzy rules and membership
simple control structure and sensorless operation. functions are tuned to give better performance.
The structure of conventional DTC drive The proposed neuro fuzzy-based controller has
originally introduced in [1], is simple. It consists been simulated using MATLAB/SIMULINK.
of torque and flux hysteresis-based controllers, The performance of the proposed controller is
flux and torque estimator switching look-up evaluated under various operating conditions.
table. Despite of its simplicity, it was well The simulation results confirm the efficacy of the
known that the implementation of the hysteresis- control system.
based DTC –PI controller requires fine tuning This paper is organized as follows : first
and cannot cope with parameters variation.. This the machine modeling of Induction Motor,
resulted in unpredictable switching frequency of second the direct torque control strategy using PI
the switching devices. Further the variable controller, third the direct torque control strategy
switching frequency will generate unpredictable using neuro fuzzy-based controller, fourth

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Simulation results at different operating λqr = Lriqr + Lmiqs (2)
conditions, fifth conclusions.
The electromagnetic torque in the stationary
2.Induction Motor Modelling reference frame is given as
The induction motor model can be
developed from its fundamental electrical and Te = (3/2)(P/2)(λdsi qs −λqsids) (3)
mechanical equations. In the stationary reference
frame the voltage equations are given by
3. DTC with PI Controller
Vds = Rsids + pλds In the DTC scheme [1] (Figure 1), the
Vqs = Rsiqs + pλqs electromagnetic torque and flux signals are
0 = Rr idr + ωr λqr + pλdr delivered to two hysteresis comparators. The
0 = Rr iqr - ωr λdr + pλqr (1) corresponding output variables and the stator
flux position sector are used to select the
Where p indicates the differential appropriate voltage vector from a switching table
operator(d/dt). The stator and rotor flux linkages which generates pulses to control the power
are defined using their respective self leakage switches in the inverter. This scheme presents
inductances and mutual inductances as given many disadvantages (variable switching
below frequency - current and torque distortion caused
by sector changes - start and low-speed operation
λds = Lsids + Lmidr problems ).
λqs = Lsiqs + Lmiqr
λdr = Lridr + Lmids

Figure 1 : Direct Torque Control scheme with PI Controller

All the schemes cited above use a PI
controller for speed control. The use of PI Fuzzy logic and artificial neural
controllers to command a high performance networks can be combined to design a direct
direct torque controlled induction motor drive is torque neuro fuzzy controller. Human expert
often characterised by an overshoot during start knowledge can be used to build an initial
up. This is mainly caused by the fact that the artificial neural network structure whose
high value of the PI gains needed for rapid load parameters could be obtained using online or
disturbance rejection generates a positive high offline learning processes. To eliminate the
torque error. This will let the DTC scheme take above difficulties, a Direct Torque Neuro Fuzzy
control of the motor speed driving it to a value Control scheme (DTNFC) has been proposed.
corresponding to the reference stator flux. At
start up, the PI controller acts only on the error The Neuro Fuzzy inference system
torque value by driving it to the zero border. (ANFIS) is one of the proposed methods to
When this border is crossed, the PI controller combine Fuzzy logic and artificial neural
takes control of the motor speed and drives it to networks[17,21,22]. Figure 2 shows the adaptive
the reference value. NF inference system structure It is composed of
five functional blocks (rule base, database, a
4. DTC with Neuro-fuzzy controller decision making unit, a fuzzyfication interface

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and a defuzzyfication interface) which are The block scheme of the proposed self-
generated using four network layers: tuned direct torque neuro-fuzzy controller
Layer 1: This layer is composed of a number of (DTNFC) for a voltage source PWM inverter fed
computing nodes whose activation functions are induction motor is presented in Figure 3. The
fuzzy logic membership functions (usually, internal structure of the NFC is shown in Figure
triangular or bell-shaped functions). 2.
Layer 2: This layer chooses the minimum value
of the inputs. In the first layer of the NF structure,
Layer 3: This layer normalises each input with sampled speed error we ,multiplied by respective
respect to the others (The ith node output is the weights wψ and wT , is mapped through three
ith input divided the sum of all the other inputs). fuzzy logic membership functions. These
Layer 4: This layer’s ith node output is a linear functions are chosen to be triangular shaped as
function of the third layer’s ith node output and shown in Figure 2.The second layer calculates
the ANFIS input signals and sums all the input the minimum of the input signals. The output
signals. The ANFIS structure can be tuned values are normalised in the third layer, to satisfy
automatically by a least-square estimation (for the following relation:
output membership functions) and a back σi = wi / (Σk wk) (4)
propagation algorithm (for output and input
membership functions). where wi and σi are the ith output signal
of the second and third layer respectively. σi is
considered to be the weights.

Figure 2 : Proposed Neuro fuzzy structure Controller

Figure 3 : Triangular membership function sets

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Figure 4 : Direct Torque Control scheme with Neuro Fuzzy Controller

The Neuro Fuzzy speed controller inverter which in turn controls the Torque
combines fuzzy logic and artificial neural parameter of Induction motor[12,15,26].
networks to evaluate the reference torque. This .
evaluation is performed using the reference 5. Simulation Results
speed and actual speed errors. This calculated Direct Torque control scheme with PI
error gives the estimated torque value which is controller and Neuro fuzzy controller are
compared with the actual torque value in the implemented in the Induction motor drive with
Hysteresis comparator. The corresponding the following parameters under various operating
torque,flux from hysteresis comparators and conditions at no load and sudden change in load
angle estimated from Flux Torque estimator are applied to the motor. All the results are
given as inputs to Switching table which represented taking time as the x-axis.
generates the appropriate voltage vector for the V=260/50Hz. Rs=0.9Ω Rr=1.227Ω P=2
Ls=150.64mH, Lr=150.64mH. Lm=143.84mH

Figure 5 : Torque Characteristics at no load with PI Controller

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Figure 6 : Torque Characteristics with sudden change in load Torque from -6.4Nm to 6.4Nm with PI
Controller

Figure 7 : Speed Characteristics at no load with PI Controller

Figure 8: Speed Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with PI Controller

Figure 9 : Stator flux response with PI controller

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Figure 10 : Magnetizing and Torque components of stator current with PI controller

Figure 11: Torque Characteristics at no load with NeuroFuzzy Controller

Figure 12 : Torque Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with NeuroFuzzy
Controller

Figure 13 : Speed Characteristics at no load with NeuroFuzzy Controller

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Figure 14 : Speed Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with NeuroFuzzy
Controller

Figure 15 :Stator flux response with Neuro fuzzy controller

Figure 16 : Magnetizing and Torque components of stator current with Neuro fuzzy controller

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Parameters PI Neuro-fuzzy
Controller Controller
Overshoot 47% 7%
Computational effort 21.6µs 97.2µs

Table I : Comparison between PI Controller and Neuro fuzzy Controller

The overshoot and time for Transcations on Idustrial Electronics, vol
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