SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 3 Issue 5 May 2016
Adaptive Fuzzy-Pi controller for speed
Control of BLDC Motor
Y Swetha1, A Srinivasa Reddy2
1pursuing M.Tech (EEE), 2working as Assistant Professor (EEE),
Nalanda Institute Of Engineering and Technology (NIET)Kantepudi(V), Sattenpalli(M), Guntur (D)522438,Andhra Pradesh.
Abstract-This paper introduces an Adaptive Fuzzypi controller for the speed control of Brushless DC
(BLDC) engine. BLDC engines have numerous
favourable circumstances over DC engines and
prompting engines. An Adaptive fuzzy controller
offers better speed reaction for start-up while PI
controller has great consistence over variety of
burden torque yet has moderate settling reaction.
Half and half controller has leverage of
coordinating a predominance of these two
controllers for better control exhibitions. In this
paper, the established PI controller is coordinated
with Adaptive Fuzzy Logic controller to make
crossover control framework with benefits of both.
Additionally it displays the similar study between PI,
Adaptive Fuzzy, and cross breed Adaptive Fuzzy-PI
controller for the same. The dynamic qualities of
BLDC Motor, for example, speed, torque, current
what's more, back EMF are examined for differed
load torque conditions through recreation under
MATLAB SIMULINK environment.
change in burden torque and the affectability to
controller additions Ki and Kp. This has brought
about the expanded interest of advanced nonlinear
control structures like Fuzzy rationale controller
which was displayed in 1965. Besides that, fluffy
rationale controller is more proficient from the other
controller, for example, PI controller [3]. These
controllers are inalienably hearty to load
aggravations.
II. PERMANENT MAGNET BLDC MOTOR
BLDC engine can be demonstrated in the 3stage ABC variables. Allude to Fig. l, the electrical
piece of BLDC engine can be spoken to in grid
structure as take after:
+
Keywords — Brushless DC motor; fuzzy logic
controller; Adaptive fuzzy -PI; inverter; PI
controller; speed control .
I. INTRODUCTION
Since 1980's new plan about lasting magnet
brushless engines has been created [1]. BLDC
engine has trapezoidal back EMF and semi
rectangular current waveform. BLDC engines are
quickly getting to be mainstream in commercial
ventures, for example, Electrical machines, HV AC
industry, restorative, electric footing, car, air ships,
military hardware, hard plate drive, mechanical
mechanization gear and instrumentation as a result
of their high productivity, high power variable,
noiseless operation, minimized, dependability and
low upkeep. The pivot of the BLDC engine depends
on the criticism of rotor position which is gotten
from the lobby sensors [2].
To supplant the capacity of commutates
and brushes, the BLDC engine require an inverter
and a position sensor that distinguishes rotor
position for legitimate substitution of current. The
motivation behind why routine controller has low
effectiveness, for example, PI controller in light of
the fact that the overshoot is too high from the set
point and it may requires delay investment to get
consistent and languid reaction because of sudden
ISSN: 2348 – 8379
(1)
Where
,
and
the stage winding
voltages, is the resistance per period of the stator
winding, while
,
and are the stage streams.
Fig. 1 Equivalent circuit for BLDC motor
The developed electromagnetic Torque can be
expressed as:
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SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 3 Issue 5 May 2016
(2)
Where
,
&
and ,
&
represent
induced electromotive forces input current to motors
in a, b & c phases and
represents angular
velocity.
II.
characterize a scope of qualities known as fluffy
enrolment capacities.
The inputs of the fluffy controller are
communicated in a few etymological levels
demonstrated later in figure 7, these levels can be
depicted as positive huge (PB), positive little (PS),
or in different levels. A fluffy rationale control
comprises of:
OPERATING PRINCIPLES
Fig. 3 PI controller block diagram.
Fig.2 Brushless dc motor block diagram
A). Fuzzification - This procedure changes over or
changes the measured inputs called fresh values, into
the fluffy etymological qualities utilized by the
fluffy thinking component.
A) Conventional PI controller
The yield of the PI controller in time
area is characterized by the accompanying
mathematical statement:
=
+
(3)
B). Knowledge Base - An accumulation of the
master control rules (learning) expected to
accomplish the control objective.
Where
C). Fuzzy Reasoning Mechanism - This procedure
will perform fluffy rationale operations and result
the control activity as per the fluffy inputs.
is the yield of the PI controller,
is
the corresponding addition,
is the basic increase,
and e(t) is the momentary blunder signal.
The principle point of interest of adding the
necessary part to the relative controller is to dispose
of the enduring state blunder in the controller
variable. In any case, the essential controller has the
genuine disadvantage of getting immersed before
long if the mistake does not alter its course.
This marvel can be maintained a strategic
distance from by acquainting a limiter with the
essential piece of the controller before adding its
yield to the corresponding controller. Fig.3
demonstrates the PI controller piece graph.
B) Fuzzy Logic controller structure
Fig.4 demonstrates the fundamental
structure of fluffy rationale controller. Fluffy
rationale's
phonetic
terms
are
frequently
communicated as intelligent ramifications, for
example, If-Then principles. These principles
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Fig.4 Fuzzy logic controller with two inputs and one
output
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SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 3 Issue 5 May 2016
C) Adaptive Fuzzy-PI controller
The motivation behind the half and
half control plan depends on pay for overshoots and
undershoots in the transient reaction and minimizing
the relentless state blunder.
In this paper I proposed Pi controllers and
Adaptive fuzzy controller and Adaptive fuzzy-Pi
controllers.
Control technique with PI controller
In this type of controlling technique a PI
controller is connected to the controlled voltage
source. The error signal obtained by subtracting the
actual speed, reference speed is given to the PI
controller. Fig. 6 shows the model of speed control
of BLDC motor using controller.
Fig.5 Hybrid fuzzy-PI controller
A PI controller when utilized as a part of
mix with FLC such that close unfaltering state
operation, PI controller assumes control over the
control taking out the detriment of the FLC.
Likewise when far from the working point FLC
overwhelms and kills the event of overshoots and
undershoots in drive reaction. The prevalence of
both fluffy and PI controller are incorporated
together by utilizing a switch as appeared as a part of
Fig.5.
III. PROPOSED SIMULATION MODELS
This paper concentrated the speed
controlling of a BLDC machine. BLDC machine
requires three phase ac voltage to operate, in order to
produce required voltages to the machine I proposed
three phase inverter.
The inverter can generate the required ac
levels to the BLDC when it has the proper firing
pulses it can generate effectively. The process of
generation of firing pulses purpose hall sensors are
also utilized to maintain the effective speed
regulation by controlling the rotor positions and
rotor angular velocity levels.
The controlled hall sensors are passed to
decoders to check the velocity levels by utilizing the
logical operators and data type conversions are
presented to generate the signals in our required
format. The generated firing pulses are connected to
inverter.
In order to maintain the constant speed
purpose we need to maintain the required voltage
levels effectively to the inverter. It is controlled by
different controllers.
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Fig.6 Simulink model of speed control of BLDC
motor with PI controller
Fig.7 speed of the BLDC with PI controller
The generated results from the BLDC
machine as given below. Fig 7 gives the generated
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SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 3 Issue 5 May 2016
speed from the machine. The speed consisted ripple
content levels at transient condition which leads to
reduction in the stability.
Control technique with Adaptive fuzzy controller
The Simulink model is given in the fig
8.the controlled voltage is regulated by the adaptive
fuzzy logic controller. The adaptive fuzzy controller
is model is given fig9.
Accordingly, 49 principles have been developed to
perform the fancied errand. "Mamdani" sort of
Fuzzy is consolidated. "Mother" (Middle of
Maximum) system for Deffuzification has been
utilized as a result of its speedier response
Fuzzy control was formerly projected as a
model-free control technique. The existence of a
(fuzzy) model of the essential process allows us to
investigate its characteristics and properties, and to
design a (fuzzy) controller concerned with to attain
convinced performance necessities.
Model-based fuzzy mechanism allow us to
assurance closed-loop steadiness (at least
theoretically), and to analyze performance and
robustness of the closed loop system, which are
significant compensations over model-free fuzzy
control.
A fuzzy model and a fuzzy controller can
be decided in control schemes known from the
adaptive control theory. Some of the main adaptive
control schemes are MRAC (model reference
adaptive control scheme) and STR (self-tuning
regulators).
As you know that fuzzy logic control based
on the human experience for the system under
consideration.So, sometimes the FLC need some
modifications.
There for a sliding mode add an efficient to
the static or normal FLC.
Adaptive Fuzzy Logic controller
Fig.8 Simulink model of speed control of BLDC
motor with fuzzy controller
Fig.9 Control Strategy of Adaptive fuzzy controller
Fuzzy controller:
FLC has two inputs to be specific Error (E)
and change in Error (CE) .The yield of this
controller is voltage (V).Input and Output variables
comprise of seven participation works each.
Triangular state of enrolment capacity has
been picked in light of its effortlessness.
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Adaptive fuzzy control (AFC) techniques
are generally used for systems with uncertainties or
systems vague information. Adaptive fuzzy sliding
mode control it is mean the controller design with
fuzzy logic which it 's performance just like SMC in
generally we say fuzzy like sliding mode controller
but adaptive fuzzy controller could be like any
controller which design with fuzzy logic such as
fuzzy like PI controller or etc.
Fig.10 beneath shows Fuzzy participation
capacities for inputs (E, CE) and yield (V) for the
fluffy controller.
It consisted Negative Small (NS), Negative
Medium (NM), Negative Base (NB),zero error (ZE),
Positive Small (PS), Positive Medium (PM), Positive
Base (PB) for both error signal and Change in error
signal and the output signal has nine membership
functions those additional two functions are utilized
to calculate the errors effective manner.
The resulting rule matrix with assigned
weights is shown in Table I.
In this controlling technique the error of
speed and change of error are given to Adaptive
fuzzy controller and 49 rules have been developed to
perform fancied errand.
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SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 3 Issue 5 May 2016
Fig.11 speed of the BLDC motor with fuzzy
controller
Control technique with adaptive fuzzy Pi controller
Proposed adaptive Fuzzy –Pi controller
based model is shown in below figure12. Operating
controlling strategy is given in fig 13.
Fig.10. Input (E, CE) and output (V) membership
functions of FLC
CE
NB
NM
NS
ZE
PS
PM
PB
NB
NVB
NB
NM
NS
ZE
PS
PM
NM
NVB
NB
NM
NS
PS
PM
PB
NS
NVB
NB
NM
NS
PS
PM
PB
ZE
NB
NM
NS
ZE
PS
PM
PB
PS
NB
NM
NS
PS
PM
PB
PVB
PM
NB
NM
NS
PS
PM
PB
PVB
PB
NM
NS
ZE
PS
PM
PB
PVB
E
TABLE 1AdaptiveFuzzy Linguistic Rules
Fig 10 gives the membership ship functions
for the adaptive fuzzy controller and table 1 gives
the information about the rules for the membership
functions and fig 11 gives the generated speed from
the machine.
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Fig.12 Simulink model of speed control of BLDC
motor with hybrid fuzzy-pi controller
It demonstrates that PI controller has high
Peak Overshoot/Undershoot, at both no heap and
with burden torque conditions. Likewise settling
time is nearly higher for PI controller. Fluffy
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SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 3 Issue 5 May 2016
controller gives a smooth reaction, with no top
overshoots and has a quicker reaction than PI
controller. Be that as it may, AFC settles with
substantial rate mistake upon burden variety.
Crossover Fuzzy-PI comprise of a 'switch
subsystem' which works such that when speed
blunder is vast and clock time is not exactly a limit
worth, control will be changed to Fuzzy and the
other way around.
The generated results of the adaptive fuzzyPi controllers are given below. Fig 14 gives the
speed and 15 gives the information about Stator
current and back EMF and fig 16 gives the
electromagnetic Torque.
Fig.13 Proposed Adaptive fuzzy- PI controller
Half and half Adaptive Fuzzy-PI controller
demonstrates a nearly better reaction for all the three
variables considered i.e. rate overshoots, settling
time, relentless state blunder at both no heap and
with burden torque considered. In the event of
Hybrid Fuzzy-PI, the reaction at first takes after the
way of AFC and when Load torque was fluctuated, it
took after the way of PI controller.
Fig.15 stator current and back emf of BLDC with
proposed Adaptive fuzzy-Pi controller
Fig.14 speed of BLDC with proposed Adaptive
fuzzy controller
Fig.16 electromagnetic torque of proposed Adaptive
fuzzy-Pi BLDC motor
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SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 3 Issue 5 May 2016
PARAMETER
Stator
Phase
Resistance
Stator
Phase
Inductance
Torque Constant
Inertia (J)
Friction Factor
Back EMF
Power
Number Of Pole Pairs
Reference speed
VALUE
2
5mh
2
0.0008Kgm2
0.001Nms
120
320
4
840
[5] Md. Firdaus Zainal Abdin, Dahaman Ishak and Anwar Hansi
Abu Hassan, "A comparative study of PI, Fuzzy and Hybrid PIFuzzy controller for speed control of Brushless DC motor Drive"IEEE International Conference on Computer Applications and
Industrial Electronics , 201 I ,Malaysia.
[6] Tan Chee Siong, Baharuddin Ismail, Md. Fayzul Mohammed,
Md. Faridun Nairn Tajuddin, Siti Rafidah Abd. Rahim, Zainuddin
Mat Isa, "Study of fuzzy and PI controller for permanent-magnet
brush less dc motor drive",IEEE International Power engineering
and Optimization Conference (PEOCO) 2010.
[7] Vishal Verma, V Harish, Renu Bhardwaj "Hybrid PI speed
Controllers for Permanent Magnet Brushless DC motor", IEEE
Conference, 2012,lndia.
AUTHOR DETAILS:
Table 1: Proposed Simulink model parameters
Y Swetha pursuing M.Tech (EEE)
from
Nalanda
Institute
of
Engineering
&Technology(NIET),Kantepudi(V),
Sattenpalli(M),
Guntur
(D)522438,Andhra Pradesh.
The adpative fuzzy Pi controller can
maintain the speed very effectively. And also it can
produce reduced ripple contents in steady state and
transient state which leads to increased stability
levels then the system performance is enhanced.
IV. CONCLUSION
BLDC engines have numerous
favourable circumstances over DC engines and
affectation engines. Be that as it may, it needs a
velocity controller for both settled rate and variable
pace applications. Three controllers have been
examined in particular PI, Adaptive Fuzzy and
proposed Adaptive fuzzy -PI.
Also, a relative investigation of the
controllers have been done which demonstrates that
PI controller has beginning Overshoot/undershoots,
moderate reaction however great execution under
Load variety with less unfaltering state mistake.
While Fuzzy controller has no Pea overshoots
Undershoots, quicker reaction yet settles with
extensive unfaltering state mistake for Load varieties.
A proposed Adaptive fuzzy Pi controller,
joining the benefits of both the above controllers (in
this way wiping out the disadvantages connected
with each of the controller) can perform well with no
Peak overshoots/undershoots, speedier reaction
what's more, slightest consistent state blunder, even
at sudden Load varieties.
C
A Srinivasa Reddy
working as
Assistant Professor (EEE) from
Nalanda Institute of Engineering
&Technology(NIET),
Kantepudi(V),
Sattenpalli(M),
Guntur (D)-522438,Andhra Pradesh.
REFERENCES
[I] R. Krishnan, Permanent magnet Synchronous and Brushless
DC motor drives, CRC press, 20 I O.
[2] H. K. Samitha Ransara and U. K. Mandawala ,"Low cost
Brushless DC motor drive", IEEE conference on Industrial
Electronics and Applicatons,20 II.
[3] Muruganantham. N, Pal ani. S, Hybrid Fuzzy-PI controller
based speed control for pmbldc motor using soft switching",
European Journal of Scientific Research, June 2012.
[4] M. V. Ramesh, 1. Amarnath, S. Kamakshaiah, G.S. Rao
"Speed control of Brushless DC motor by using Fuzzy Logic PI
controller", ARPN journals,20 II.
ISSN: 2348 – 8379
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