Abstract and it is utilized to analyze

 

 

Abstract

In this document a fuzzy
logic based controller is produced for the given BLDC engine speed control. At
first a pi controller is produced for the speed control of the given BLDC
motor. The numerical model of the BLDC motor is produced and it is utilized to
analyze the representation of the controllers. By broad reenactments it is
watched that the representation of the fuzzy logic controller is better than
some other controllers. Fuzzy Logic Controller demonstrates to give a better
representation compared to the PID controller as far as time overshoot and percent
adjustment and better control of the DC engine since FLC did not require any
understanding and human controls are decreased. The representation of the two
strategies is assessed and compared as far as adjustment time (Ts) and greatest
overshoot under various load conditions.

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1. Key words

 

Fuzzy control, Brushless
DC motor, PID controller, Rotor reluctance, motor rotor, windings, synchronous motor,
trapezoidal wound, torque, PWM,

 

2.  INTRODUCTION

 

 

The sinusoidal back-EMF is called lasting magnet synchronous
engine (PMSM).Unlike the regular controllers the present control strategy
utilized as a part of this paper depends on a typical DC flag and just a single
current controller is utilized for the three stages. A criticism flag relative
to armature voltage is likewise given to the controlling circuit to establish a
blunder motion for working the control to manage the voltage to the engine and
in this way the engine speed. DC engine control is by and large acknowledged by
modifying the terminal voltage connected to the armature however different
strategies, for example, changing the field resistance, embeddings a resistor
in arrangement with the armature circuit are likewise accessible. Utilizing
this set up the external control (speed control circle) circle of the BLDC
drive framework is controlled utilizing PI, PID and fuzzy logic controller and
execution is assessed. In the external speed control circle PI, PID and fuzzy
logic based controllers are utilized and in the present control circle PI
controller is utilized. The control incorporates a managing circuit that having
a yield for controlling the armature voltage to the engine. Two principle
issues experienced in engine control are the time-shifting nature of engine
parameters under working conditions and existence of noise in framework circle.
In view of their high reliabilities, adaptabilities and low costs, DC engines
are generally utilized as a part of modern applications, robot controllers and
home apparatuses where speed control of engine are required.

 

The speed control is one of the imperative segment in Direct
Current engine (DC engine) operation. In view of fuzzy logic, a fuzzy
controller changes over a linguistic

 

Control methodology into a programmed control procedure, and
fuzzy tenets are developed by master understanding

 

or on the other hand learning database. BLDC engine is
ordinarily characterized as a perpetual magnet synchronous engine with a
trapezoidal back EMF waveform shape. The stator twisting of BLDC engine is
regularly trapezoidal injury with a specific end goal to produce the
trapezoidal shape back-EMF waveform. The BLDC engine has the trapezoidal
back-EMF waveform. There are two sorts of perpetual magnet BLDC engines, which
rely upon their back-EMF waveforms.

3. Overview

 

A. Brush less Dc motor

 

The electric motor is a motor that transform electrical energy into
mechanical energy. Generally,
it can be said that applied voltage affect speed while torque is controlled by
current. DC motor used in railway engines, electric cars, elevators, robotic
applications, car windows and wide varieties of small appliances and complex
industrial mixing process where torque cannot be compromised

 

 

 

 

Fig.1
Disassembled view of a BLDC motor

 

 

 

       
Fig. 2. Inverter fed 1200 BLDC motor drive

 

 

The basic
diagram of conventional 1200 BLDC motor drive. Gating signals to
each inverter switching are given based on rotor position.

 

 

 

 

 

B.
Purpose for controlling motor                
                                     

 

In mechanical autonomy
speed, control is critical on the grounds that robots can work appropriately
just if engines speed is controlled in precise way. One case is CNC machine in
which 1 mm of blunder can’t be compromised so DC engine in such case gives
correct speed control. The reason to control the speed of engine is on account
of there are numerous application in which client needs to change the speed of
engine to get certain undertakings. Subsequently, it is neglectful that without
speed controllers we can’t get our objective in mechanical and modern
applications. The speed control regularly done by criticism speed controllers
or shut circle speed controllers.

C.  MATHEMATICAL MODEL OF BLDC MOTOR

 

Accepting
further that there is no adjustment in the rotor reluctances with an edge, he
trapezoidal back-EMF wave shapes are demonstrated as a component of rotor
position so rotor position can be effectively ascertained by the operation
speed. Thus the circuit conditions of the three windings in stage factors
depend on the condition, the comparison circuit of engines can be gotten, it
has been expected that the stator resistance of the considerable number of
windings is equivalent in fig 3.

 Fig. 3. The equivalent circuit of BLDC
motor

 

The
BLDC motor has three stator windings and permanent magnets on the rotor. Since
both the magnet and the stainless steel retaining sleeves have high
resistivity, rotor-induced currents can be neglected and no damper windings are
modeled

The
back EMF’s are expressed as a function of rotor position, and are the function
of rotor position. The trapezoidal shape functions with limit values between +1
and -1.

 

 

 

 

 

 

 

D.  SPEED CONTROL OF BLDC MOTOR

 

Fig.4 represent the
complete block diagram of three phase brush less dc motor drive system.

 

 

 

                   Fig. 4. BLDC motor drive system

The drive consists of speed
controller, current controller, commutation logic and the voltage source
inverter. This is then compared with its reference value and the current error
is processed in PI current controller to generate PWM pulses for all the six
valves of the inverter. In the speed control part the speed of the motor is
compared with its reference value and the speed error is processed. In this
paper the current control part of the BLDC drive system is implemented as
follows. The inner current control loop synchronizes the inverter gates signal
with the electromotive forces. The PI controller is widely used in industry for
speed control due to its ease in design and simple structure. Figure 5 shows
the current controller block diagram

 

         
Fig. 5. Current controller block diagram

3. PID CONTROLLER

 

To
reduce the overshoot and settling time we then used a PID controller. Fig.6
shows the Proportional Integral Derivative (PID) controller block diagram.

 

 

 

 

       
Fig. 6. PID controller block diagram

 

The integral controller reduces the rise time, causes an
overshoot, increases the settling time and most importantly eliminates the
steady state error. The transfer function of the most basic form of PID
controller is, where Kp is the proportional gain, Ki is the integral gain and
Kd is the derivative gain. Figure shows the Proportional Integral Derivative
(PID) controller block diagram. Proportional (P), integral (I) and derivative
(D) are the three main parameters of the PID controller. The proportional,
integral and derivative terms are summed to calculate the output of the PID
controller.

 

Kp = proportional gain

Ki = integral gain

Kd = derivative gain

        Ti = integral time

Td = derivative time

The proportional controller stabilizes the gain but produces a
steady state error. The integral controller reduces the steady state
error.  The values of these three
parameters interpreted in terms of time ,where ,’P’ depends on the present
error, ‘I’ on the accumulation of past errors and ‘D’ is a prediction of future
errors, based on current rate of change.If the controller is digital, then the
derivative term may be replaced with a backward difference and the integral
term may be replaced

With a sum. By tuning the three parameters in the algorithm of
PID controller.

The controller can provide control action designed for specific
process requirements. Control signal U(t) is a linear combination of error E
(t), its integral and derivative. Figure shows the schematic model of a control
system with a PID controller.

 

For a small constant sampling time (Ts), Can be
approximated as:

 

 

 

4A. TUNING PID PARAMETER

 

Hand tuning is based on definite rules of thumb
used by experienced process engineers Table A. The tuning is a settle between
fast reaction and stability.

 

table
A. Hand tuning rules

 

 

 

 

 

 

Operation

Rise Time

Overshoot

Stability

 

 

 

 

Kp ?

Faster

Increases

Decreases

 

 

 

 

Td ?

Slower

Decreases

Increases

 

 

 

 

1/Ti

Faster

Increases

Decreases

 

 

 

 

 A simple
hand-tuning procedure is as follows:

i.         
Remove derivative and integral actions by
setting Td = 0 and 1/Ti = 0.

ii.        
Tune Kp such that it gives the desired response
except the final offset value from the set point.

iii. Increase Kp slightly and adjust Td to dampen the overshoot.

iv. Tune 1/Ti such that final offset is removed.

 

4B. Simulink model
of PID controller

 

Firstly without using any load, then
with a load. The model simulated by setting the parameters summarized in Table
In fig 7

 

 

Fig 7. PID (hand tuning) controller output of DC motor with &
without load

 

PID (hand tuning); we observe the overshoot,
small rise time and large settling time shown in fig 8

 

Fig.
8. Response of a tune PID controlled system at no load

 the load applied
in the first second after the DC motor running using PID controller. We notice
that the speed reach the desired value 1 rad/sec but there is an overshoot and
it is significant that the speed is decrease below and increase above for
approximately 0.2 rad/sec, take 4 seconds applying the load and lastly settle.
Hand tuning is based on certain rules of thumb used by experienced process
engineers Table A. The tuning is a compromise between fast reaction and
stability.

5. FUZZY
LOGIC CONTROLLER

 

Fuzzy logic’s linguistic terms are regularly communicated as
logical ramifications, for example, If-Then guidelines. The contributions of
the fuzzy controller are communicated in a few linguistic levels appeared in
Figure. Fuzzy participation capacities might be as triangle, a trapezoid, a
chime as shows in Figure demonstrates the fundamental structure of fuzzy logic
controller. These levels can be depicted as positive huge (PB), positive medium
(PM), positive little (PS), or in different levels. Later for comparison reason
we executed a fuzzy logic controller. Each level is depicted by a fuzzy set.
Fuzzy logic is communicated by methods for the human dialect .Based on fuzzy
logic, a fuzzy controller changes over a linguistic control system into a
programmed control methodology, and fuzzy tenets are built by master
understanding or information database. The fuzzy surmising mechanism in this
investigation takes after as: The Fuzzy Logic controller consists of four
fundamental parts: fuzzification, an information base, derivation motor, and a
de fuzzification interface. In the de fuzzification interface, a real control
activity is acquired from the consequences of fuzzy surmising motor. To begin
with, set the mistake e (t) and the blunder variety d e(t) of the rakish speed
to be the information factors of the fuzzy logic controller. The outcomes got
by fuzzy logic rely upon fuzzy derivation rules and fuzzy ramifications
administrators. The control voltage u (t) is the yield variable of the fuzzy
logic controller. In the fuzzification interface, an estimation of information
sources and a change, which changes over info information into reasonable
linguistic factors, are performed.

 

Which impersonate human decision-production. The learning base
gives important data to linguistic control rules and the data for fuzzification
and de fuzzification. Each component affects the effectiveness of the fuzzy
controller and the behavior of the controlled system.

                           Fig. 9. FLC architecture

 

 

 

Error

 

 

 

 

 

 

 

 

 

 

  error

 

 

 

 

 

 

 

 

NL

NS

 

Z

PS

PL

 

 

 

 

 

 

 

NL

NL

NL

 

NS

NS

Z

in

 

 

 

 

 

 

 

 

 
Changing

NS

NS

NS

 

NS

Z

PS

 

 

 

 

 

 

 

Z

NS

Z

 

Z

Z

PS

 

 

 

 

 

 

 

PS

NS

Z

 

PS

PS

PL

 

PL

Z

PS

 

PS

PL

PL

 

 

 

 

 

 

 

 

Table B: fuzzy
associative memory table for dc motor control

 

Fig. 10. Simulink model of FLC controller

 

 

 

                                                                                                                

6.  RESULTS AND DISCUSSION

 

 

It
is clear that the response obtained using the fuzzy logic controller is better
than the response of PID.

(Hand
Tuning) controller method in term of settling time and overshoot. The PID
controller when properly tuned responds faster to the input parameter, but
there are an overshoot and approximately double settling time than FLC. The
fuzzy logic based controller has a sluggish response to the input signal. The
result shows that using PI, PID controller, the system is having a settling
time of 0.01967 sec and 0.01548 sec respectively and an overshoot of 20.15% and
19.2% respectively but using a Fuzzy controller the system is having a settling
time of 0.01406 sec and an overshoot of 17.05%. From the above result it is
shown that a fuzzy logic controller has better performance. The performance of
the system using PI, PID and Fuzzy controller at reference speed of 2000 rpm in
loaded condition. Performance comparison of PI, PID and Fuzzy controllers at
different speeds under loaded conditions is shown in below Table. The PI
controller has some disadvantages such as high starting overshoot, sensitivity
to controller gains Ki and Kp and sluggish response due to sudden change in
load. Fuzzy logic controller is more efficient from other controllers such as
PI and PID controller.

When
a PI controller is used in the outer speed loop, it reaches steady state time
quickly. But there is overshoot in the response, so in order to reduce that a
PID controller is used. This controller reduces overshoot as well as setting
time. When a fuzzy logic controller is used the overshoot and settling time are
reduced further.

 

 Fig.11. Speed Response of the BLDC motor with
PI, PID and Fuzzy controller at 1000

 

 

 

 

Fig.12.
Speed Response of the BLDC motor with PI, PID and Fuzzy controller at 2000 rpm

 

This is the
table of speed v/s Settling time table which will give us the understanding
with PID AND FLC

 

 

Speed

    PI

     PID

FLC

(rpm)

 

 

 

 

 

%

ts

%

ts

%

ts

 

Mp

(ms)

Mp

(ms)

Mp

(ms)

1000

45

19.2

43

15.29

38

14.09

2000

20.15

19.67

19.2

15.48

17.05

14.06

3000

6.07

19.76

5.60

15.72

4.77

14.1

4000

1.93

1.63

17.38

0.77

15

 

       TABLE C: PERFORMANCE COMPARISON

 

 

                        7. CONCLUSION

 

The performance of a three-phase BLDC system using a PI,
PID speed controller and fuzzy logic speed controller was evaluated. Through a
large number of simulation shows that the performance of fuzzy logic controller
is better than PI and PID controller. The range of future as a fuzzy logic can
be combined with PID control and performance can be assessed. We have
demonstrated that the single neuron fuzzy.

Self-adaptive
PID applied to speed control of the brushless DC motor would lead to the
control system performance Improvement, the overshoot can be suppressed to zero
and the regulation time can be decreased to 0.6 s, much better than other two
algorithms. Experiment result verified that the algorithm can be applied to
real-time actual control system.

 

8. Future Recommendation

It is concluded
that future investigation are required with respect to the application of the
structural programming and stability problem in fuzzy control systems

 

                 

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