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.

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