Speed Control of PMBLDC motor using Fuzzy Logic · PDF fileSpeed Control of PMBLDC motor using Fuzzy Logic Controller with ... with PMSM which has a sinusoidal back EMF. ... The above

International Journal of Advanced Engineering Research and Technology (IJAERT) Volume 4 Issue 1, January 2016, ISSN No.: 2348 – 8190

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Speed Control of PMBLDC motor using Fuzzy Logic Controller with

Sensorless Technique

Mohammad Zaid1, Mohammad Ayyub

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1,2Department of Electrical Engineering, Zakir Hussain College of Engineering & Technology, Aligarh Muslim

University – Uttar Pradesh, India

Abstract Recent advances in the field of power electronics have

made PMBLDC motors very popular. They are being

used in host of applications because they posses

certain desirable features as compared to brushed DC

motor and servo motors. This paper presents speed

control of PMBLDC motor using Fuzzy logic

controller with sensorless operation of motor. The

simulation is carried out in MATLAB/SIMULINK

platform. The simulation result compares the

performance of Fuzzy Logic Controller with PI

controller.

Keywords: Fuzzy logic controller, PMBLDC motor, PI

controller, DC motor.

I. INTRODUCTION The first electronically commutated brushless DC

motor was developed with the help of Hall elements

in1962 [11], since then tremendous development has

been made in the field of power electronics and

permanent magnet materials. Today PMBLDC motor

is used in many applications from aerospace,

automobile industry to household appliances. The

PMBLDC motor posses certain desirable features such

as high efficiency, high power factor, lower

maintenance, precise and accurate control and high

power density. The PMBLDC motor generally has a

trapezoidal back emf which is different as compared

with PMSM which has a sinusoidal back EMF. The

PMBLDC motor is developed on the basis of brushed

DC motors, but unlike brushed DC motors the

commutation is electronically controlled and Hall

sensors are used for sensing the rotor position. The

output of Hall sensors are used for generation of

switching signals for the inverter. Hall sensors are

costly and less reliable especially in space application.

Due to these reasons various sensorless techniques

have been developed. Each of the sensorless

techniques employed have their own advantages and

disadvantages. These sensorless techniques are used to

detect the rotor position of the motor indirectly. Most

popular and widely used technique is back emf

detection using line voltage difference method.

Actually in any sensorless scheme we need to identify

exact commutation instants for the generation of

virtual Hall signals, in the scheme using difference of

line voltage the difference of two line voltages gives

the back emf of any one phase. The zero crossing

instants of that phase emf waveform gives the

approximate commutation instants of the current of

that phase. The zero crossing instants need to be phase

shifted to get the exact commutation point. A low pass

filter generally introduces the delay required for the

operation. Most of the back emf detection techniques

suffer from serious drawback that at low speeds it is

difficult to detect the back emf, hence some starting

methods needs to be employed before motor

accelerates to minimum threshold speed. Another

improved method for detection of rotor position is the

utilization of third harmonic component in the EMF

waveform of the motor. The voltage between the

artificial neutral and motor neutral gives the third

harmonic voltage component which contains the

information about the zero crossing instants of back

EMFs of the three phases. It can be shown that [9] this

voltage between the two neutrals is numerically equal

to mean of three EMFs. Zero crossings of third

harmonic voltage when properly processed

corresponds to exact commutation instants which is

needed for proper switching of inverter. In most of the

cases motor neutral is not accessible hence midpoint of

DC link can also be used for generation of third

harmonic voltage [9], but this signal is more noisy as

compared to the previous signal obtained between the

two neutrals.

In this paper we have employed sensorless technique

based on the line voltage difference method. We have

also used Fuzzy logic based controller for speed

control of motor. For comparison we have used a PI

controller and then fuzzy logic based controller. The

problem with conventional controllers comes when

either plant structure is unknown or if known is so

complex that design of controller by classical approach

would be impractical and cumbersome. The other

problem comes when model of a system is highly non

linear or rate of parameter change of plant is extremely

high. Fuzzy controllers perform very well in the

situations described above because by using FLC we

need not to know the plant structure and also by time

needed for design of controller may be significantly

shortened. However performance improvement using

FLC will depend on tuning and choosing a appropriate

rule base for FLC.

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II. SYSTEM CONFIGURATION Consider a star connected PMBLDC motor whose

stator is star connected. The motor stator is fed by a

three phase inverter which is operated in 1200

mode.

Only two phase conduct at a time and third phase is

floating. The switches are triggered utilizing the exact

rotor position of the motor .Figure 1 shows the overall

system configuration of the drive system. The speed

controller used can be a PI controller or a Fuzzy logic

controller. The control loop has outer speed controller

and inner current controller. Table 1 gives details of

PMBLDC motor specifications.

Fig.1 Overall PMBLDC motor drive

Table 1 The PMBLDC motor specifications

III. BACK EMF ZERO CROSSING

ESTIMATION In this method the zero crossing point of back emf is

estimated using the difference in line voltage.Zero

crossing points will give us the virtual hall signals

needed for the proper switching of the inverter

switches. Consider the voltage of phase a of the motor

with respect to neutral point as

Van= Ria + (L-M) ia +Ea (1)

Similar equations can be written for phase b and c .

Vbn= Rib + (L-M) ib +Eb (2)

Vcn= Ric + (L-M) ic +Ec (3)

From these equations line to line voltages can be found

Vab = R(ia-ib) + L (ia-ib) +ean-ebn (4)

Vbc= R(ib-ic) + L (ib-ic) +ebn-ecn (5)

Vca=R(ic-ia) +L (ic-ia) +ecn-ean (6)

Now to find the difference in line voltage subtract

equation five from four. No neutral point is required

for estimation of line voltages.

Vabbc= R(ia-2ib +ic) + L (ia-2ib+ic) + ean -2ebn+ecn (7)

Now consider a situation in which phase a and phase c

is conducting and phase b is open. In this situation ean

= -ecn. Therefore, in that interval (7) may be simplified

as

Vabbc= ean-2ebn+ecn = -2ebn (8)

The above result shows that the difference in line

voltage Vabbc gives the inverted and magnified

waveform of back emf of phase b. Similarly Vbcca and

Vcaab gives the inverted and magnified back emf wave

forms of phase c and a. The above derivation shows

that zero crossing of back emf can be estimated

indirectly by proper processing of three stator voltages.

A low pass filter is generally used for removing the

high frequency components present in the derived back

emf waveform. The other advantage we get by using

low pass filter is that sufficient amount of delay is

produced which gives exact commutation instants for

the generation of virtual hall signal.

IV. IMPLEMENTATION OF FUZZY

LOGIC BASED SPEED CONTROLLER The FLC scheme observes the pattern of the speed

loop error and correspondingly updates the output of

the controller to match the actual speed with the

reference speed. The triangular membership function

with 5 linguistic variables and 25 rules are used in the

FLC design. We have chosen a linear rule base which

is widely accepted with triangular membership

functions. All membership functions (MF’s) for

controller inputs, i.e., error (e) and change of error

(Δe) incremental change in controller output Δu for PI-

type FLC are defined on common interval [-1,1]. Each

of the rules of FLC is characterized with an IF part

called antecedent and then part called consequent. We

have taken three scaling factors namely Ke, Kce and

Kdu. These scaling factors are very important for

tuning of FLC because once membership functions

along with rule base are defined they cannot be

changed every time. Hence to get the optimal response

we have to tune these scaling factors until we get the

Parameters Symbol Value Units

Resistance R 2.875 Ohms

Inductance L 2.7 mH

Back-emf-

constant

ke 0.42 V/rad/s

Torque

constant

kt 0.042 N-m/A

Viscous

Damping

B 0.000089 N-

m/(rad/s)

Rotor

Inertia

J 0.0005 Kg-m2

Number of

Poles

P 4 -



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desired response. However every Fuzzy controller

design should tend to solve a control problem with a

minimal number of Fuzzy sets. If by succeeding to

solve a problem with a 5*5 Fuzzy rule base rather than

a 7*7 Fuzzy rule base, the processing of 25 instead of

49 rules will save a lot of computing time.

We have chosen 25 rule base system. Also it may seem

that a larger number of Fuzzy sets will result in a better

designed controller, practical experience has proven

that the number of Fuzzy sets involved is not so

important. The 25 rules along with their meaning used

in Table 2 gives Fuzzy inference system. Table 3 gives

meaning of linguistic variables of Fuzzy inference

system.

Table 2: Rule table for Fuzzy inference system

Table 3: Meaning of linguistic variables in Fuzzy

inference system

NVB Negative very big

NB Negative big

NM Negative medium

NS Negative small

Z Zero

PS Positive small

PM Positive medium

PB Positive big

PVB Positive very big

e Speed error

ce Change in speed error

Above rule base in words can be defined as “IF e is

NB and ce NB then Δu(output change) is NVB”.

Figure 2 and 3 shows the membership functions of

error and change in error in speed. Figure 4 shows the

overall design of Fuzzy logic controller with scaling

factors.

Fig.2 Membership function for input variable “e”

Fig.3 Membership function for input variable “ce”

Fig.4 Fuzzy Logic based speed controller

Figure 5 and 6 shows the membership function of

output variable and relationship between input and

output variables.

Fig.5 Membership function for input variable “e”

Fig.6 Surface showing relationship between e, ce and

Δu based on rule base



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Figure 7 shows the modelling of back emf waveform

using look up tables.

Fig.7 Modelling of back emf’s

V. SIMULATION RESULTS AND

DISCUSSION

The performance of the developed PMBLDC motor is

simulated in Simulink. Simulation results of motor

speed, current and emf along with PWM signals are

obtained. Figure 8 shows Simulink based model of

PMBLDC motor drive system.

Fig. 8 PMBLDC motor model with PI and FLC based

speed controller

The motor is first started with sensors and is then

switched over to sensorless control at 200ms. The

main problem in this method is detecting the actual

zero crossing because of the spikes present in the

voltage, hence an appropriate filter design is necessary.

The other problem is to determine the instant when the

control is shifted from sensor control to sensorless

control, practically this is done by first exciting two

phases out of three for a predetermined duration called

prepositioning time, which may be fixed on the inertia

of motor and its load capability. At the end of the

predetermined period motor have moved from an

unknown position to a predetermined position. Figure

9 shows the line voltage difference Vabbc which gives

zero crossing for back EMF of phase a. Figure 10

shows the zero crossing estimated by difference in line

voltage method. Filtering helps in acquiring accurate

commutation instants. Exact commutation instant will

be 30 degree phase shifted from zero crossing point

The exact commutation instants are being shown in

figure 11. A low pass second order filter with cut off

frequency of 24 Hertz is being used for filtering. From

the above figure it can concluded that zero crossing

estimated by difference of line voltage is the real

commutation instant which we require for generation

of virtual hall signal. Figure 12 and 13 gives

comparison between real hall signal originally

generated by sensors and virtual hall signals generated

by detecting exact commutation instants, both signals

should exactly match for satisfactory operation of

motor in sensorless control.

Fig.9 Line Voltage difference Vabbc

Fig. 10 Estimation of ZCP of phase b from line voltage

difference Vabbc



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Fig. 11 Estimation of ZCP of phase b from filtered line

voltage difference Vabbc

Fig.12 Virtual hall signal b

Fig.13 Real hall signal b

Figure 14 shows the motor position in radians in

sensorless control.

Fig.14 Rotor position in radians

The performance of PI controller with sensorless

control is evaluated in this section. Load torque is

applied at 0.2 seconds. From figure 15 and 16 it can be

seen that there is a significant reduction in set point

speed with application of load torque. Speed is reduced

to 194 rad/s from set point speed of 200 rad/s. Motor

speed again reaches set point speed after significant

delay of 0.3 seconds.

Fig. 15 Speed response with PI controller (Tl=1Nm)


Fuzzy Logic controller with sensorless control is

employed here. The load torque of 1Nm is applied at

t=0.2 second, similar to what we have done with PI

controller .The speed response in figure 17 shows that

there is almost no reduction in speed of the motor

when load torque is applied at t=0.2 second which

shows the superiority of fuzzy logic controller if

properly tuned over PI controller. It can be seen from

figure 18 that the reduction in speed after application

of load torque is less than 1 rad/s, and motor gets back

to set point speed almost instantaneously. This is great

improvement over PI controller which takes almost 0.3

seconds to get back at same speed reference for same

amount of load torque applied. Figure 19 shows the

speed response of motor using fuzzy logic controller

when step change in reference speed is made .

Fig. 17 Speed response with FLC (Tl=1Nm)



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Fig.19 Set point speed increased from 200 rad/s to 300

rad/s with FLC.

The trapezoidal waveform of back emf of phase a and

current of phase a is shown in figure 20 and 21.

Fig.20 Back emf phase a

Fig.21 current phase a

VI. CONCLUSION A detailed Simulink model of PMBLDC motor with

and without Hall sensors has been developed and its

speed is controlled by using both Fuzzy logic and PI

controller Motor is found to be running smoothly in

sensorless operation and all the waveforms i.e. motor

phase currents, back Emf, rotor position has been

obtained from Simulink model. Speed control using

both Fuzzy controller and PI controller has been done.

The use of Fuzzy controller has generally reduced the

rise time and settling time of the speed response of the

motor. Hence a tuned Fuzzy controller has

outperformed conventional PI controller. However the

main advantage of using Hall sensors is that motor

design remains simple, and no extra circuitry is

needed.

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Speed Control of PMBLDC motor using Fuzzy Logic · PDF fileSpeed Control of PMBLDC motor using Fuzzy Logic Controller with ... with PMSM which has a sinusoidal back EMF. ... The above