San Kar Final

8/12/2019 San Kar Final

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Intelligent Speed Controller for

BRUSHLESS D.C MOTORS

SEMINAR PRESENTATION

GuideMs. Rinu Alice Koshy

Assistant Professor

Presented by,M. Sankar

Reg No:11012477

S7 EEE

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The seminar focuses on the design and implementation of fuzzy

based PID controller for BLDC Motors in order to keep the speed

of the motor to be constant when the load varies. The

effectiveness of the same in speed control of BLDC on load

variation is also proven by simulation

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Rotor: Permanent magnet

Stator consist of a number of windings. Current through

these winding produces magnetic field and force

No commutator ,the current direction of the stator conductoris controlled electronically

Stator current is commutated through electronic switches to

appropriate phases

Hall sensor used to determine the rotor position duringcommutation

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Halls Sensors sense the position of the rotor and feeds to

decoder circuit

The Decoder Circuit turns appropriate switches on and off

The voltage through the specific coils turns the motor

Table 1: CLOCKWISE ROTATION SEQUENCE Fig 1: BLDC Motor Working Animation 4


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Fig 2: Voltage Source Inverter 5


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Fig 3: Block Diagram of speed control of BLDC Motor

Two control loops:

The inner loop synchronizes the inverter gates signals with theelectromotive forces.

The outer loop controls the motor's speed by varying the DC bus

voltage.

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A PID controller is simple three-term controller {P- Proportional,

I- Integral, D-derivative }. The transfer function of the most basic

form of PID controller ,is

Where KP= Proportional gain, KI= Integral gain and

KD= Derivative gain.

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Error Signal: e

The Control signal ufrom the controller to the plant is equal to

the Proportional gain (KP) times the magnitude of the error plus theIntegral gain (KI) times the integral of the error plus the Derivative

gain (KD) times the derivative of the error.

The output of PID controller will change in response to the error

Fig 4: Simulation model of PID Controller

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In close loop response the four important characteristics are:1)Rise time: Time taken by output to cross 90% of desired level for the

first time.

2)Peak Overshoot : How much the peak level is higher than the steady

state level

3)Settling time: time taken for the system to converge to its steady state

4)Steady state error: The difference between the steady-state output

and the desired output

For optimal performance the PID controller must satisfy the following

criteria:

Less Settling time less

Low Overshoot and rise

Steady-state error less than 1%

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Typical steps for designing a PID controller are

i) Determine what characteristics of the system needs to be

improved.

ii) Use KP

to decrease the rise time.

iii) Use KDto reduce the overshoot and settling time.

iv) Use KI to eliminate the steady-state error.

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The design of the BLDCM drive involves a complex processsuch as modeling, control scheme selection, simulation and

parameters tuning etc.

PID controller working is not good for non-linear andcomplex systems

Conclusion: Fuzzy PID control method is opted as it is a better

method of controlling complex and unclear systems.

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Fuzzy logic is a form of many-valued logic or probabilistic

logic; it deals with reasoning that is approximate rather than fixed

and exact.

The fuzzy logic, unlike conventional logic system, is able to

model inaccurate or imprecise models.

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In fuzzy logic we define human readable rules to form

the target system. For instance assume we want to

control the room temperature, first of all we define

simple rules:

If the room is hot then cool it down

If the room is normal then don't changetemperature

If the room is cold then heat it up

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Fig 5: Fuzzy Logic Working 14


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Linguistic variables are the input or output variables of the

system whose values are in natural language.

Example:

The room is hotlinguistic value

How much it is hotlinguistic variable

Fuzzy logic variables may have a truth value that ranges in

between 0 and 1.17


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A fuzzy control system is a control system based on fuzzy

logica system that analyzes input values in terms

of logical variables that take on continuous values between 0

and 1.

Fuzzy logic is a logical system which is much closer to human

thinking and natural language than traditional logical systems

Fuzzy control can be described simply as "control with

sentences rather than equations"

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The Values of Kp, Ki and Kd values of PID Controller is shown

in below Table 2 are obtained by using the ZN method.

Speed controlled indirectly by controlling the Voltage Source

inverter.

Fuzzy logic controller output is the inner dc Voltage controller.

The Voltage is controlled by varying the dc voltage.

Table 2: PID VALUES

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The fuzzy control rule is in the form of:

IF e=E and de=CE Then UPD = O/P.

These rules are written on rule base look-up Table 3. This

rule base structure is Mamdani type

Table 3: FUZZY RULES 20


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Linguistic variables which implies inputs and output have

been classified as: NB, NM, NS, Z, PS, PM, PB.

Negative Big (NB), Negative Medium (NM), Negative

Small (NS), Zero (Z), Positive Small (PS), Positive

Medium (PM), Positive Big (PB).

Inputs and output are all normalized in the interval of [-10,10] as shown

Fig 6: Triangular Membership Function 21


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The output equation obtained by Defuzzification by Centre of

Gravity Method is:

This is the fuzzy PID controller signal given to voltage source

inverter.

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The fuzzy rules are extracted from knowledge of PID controller

and human experience about the process. These rules contain the

input/the output relationships that define the control strategy. Each

control input has seven fuzzy sets so that there are at most 49 fuzzy

rule.

Table 3: FUZZY RULES23


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Fig 7: Fuzzy PID Controller

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Characteristics of motor, 1500 rpm with no load

tr %Mp ts tr %Mp ts

1500

no-

load

0.0202 16.53 0.35 0.0061 13.13 0.10

Speed PID Controller Fuzzy PID Controller

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Characteristics of motor, 1500 rpm with load of 5N

tr %Mp ts tr %Mp ts

1500

load

0.0209 15.53 0.40 0.0077 3.6 0.15


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Step down Characteristics of motor,1500-1000 rpm with no load

tr %Mp ts tr %Mp ts

1500

no-load

0.0202 16.53 0.35 0.0061 13.13 0.15


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Step down Characteristics of motor,1500-1000 rpm with load

tr %Mp ts tr %Mp ts

1500

load

0.0209 15.53 0.35 0.0077 3.6 0.15


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Fuzzy PID Controller PID Controller

Conventional PID controlleralgorithm is simple, stable,

easy adjustment and high

reliability.

Tuning PID control parametersis very difficult, poor

robustness, therefore, it's

difficult to achieve the optimal

state under field conditions in

the actual production

When load varies it becomes

unstable, give more overshoot.

It can work with less preciseinputs.

Tuning of fuzzy PID controller

is easy ,more robust than other

non-linear controllers.Fuzzy controllers have better

stability, small overshoot, and

fast response.

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Require more fine tuning and simulation before operational.

If the a reliable expert knowledge is not Available , or If the

controlled system is too complex to derive the required

decision rules, development of a fuzzy logic controller become

time consuming and tedious or sometimes impossible.

A fuzzy logic controller cannot be achieved by trial and- error

method. Only ZN method is used.

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With results obtained from simulation, it is clear that for the

same operation condition, the BLDC speed control using

Fuzzy PID controller technique had better performance than

the conventional PID controller.

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REFERENCES

[1] Dr. R Arulmozhiyal An Intelligent Speed Controller for Brushless DC Motor

2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)

[2]Atef Saleh Othman Al-Mashakbeh, Proportional Integral and Derivative Control

of Brushless DC Motor, European Journal of Scientific Research 26-28 July 2009,

vol. 35, pg 198-203.

[3]Q.D.Guo, X.MZhao BLDC motor principle and technology application[M].

Beijing: China electricity press, 2008.

[4]K. Ang, G. Chong, and Y. Li, PID control system analysis, design and

technology, IEEE Trans.Control System Technology, vol. 13, pp. 559-576, July

2005.

[5] C Zhang. BLDC motor principle and application [M].Beijing: Machinery Industry

Press, 1996.

[6] J.E Miller, "Brushless permanent-magnet motor drives," Power Engineering

Joumal, voI.2, no. 1 , Jan. 1988. 33


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