Space Robotics 470

8/3/2019 Space Robotics 470

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PAPER PRESENTATION ON

PRESENTED BYPRESENTED BY

k.swathik.swathi

08N11A044308N11A0443

BACHELOR OF TECHNOLOGYBACHELOR OF TECHNOLOGY

ELECTRONICS & COMMUNICATIONELECTRONICS & COMMUNICATION

ENGINEERINGENGINEERING

Asst.professor

Asst.professor


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OVER VIEWy This paper deals with the some aspects of design and analysis of

Fuzzy Controlled PLL.

y It considers control of the loop gain by studying the phase variationbetween the two signals.

y The fuzzification deals with triangular membership functions forphase angle of the input signal and the voltage Vdc of the outputsignal.

y Fuzzy interference is drawn using IF-THEN rules. Defuzzification iscarried out using height defuzzification method.

y

We report improvement in SNR and the lock in range frequency of afuzzy controlled PLL as compared with that of classic PLL.


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Fin VdFou

PHASEDETECTOR

LOW PASS FILTER

VOLTAGECONTROLOSCILLATOR

Feed back path

PLL block diagram


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DESIGN ASPECT OF CLASSIC PHASE LOCKED LOOP:


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POSSIBILITIES OF FUZZY CONTROL IN PLLs:

y Control of the loop gain:

For the analog PLLs, probably the simplest method to

control the loop is that of changing the loop gain and thus inputcontrol voltage to the VCO.

y FUZZYCONTROL OF THE PHASE COMPARATOR:

The advantage of the fuzzy control of the PhC is the ofease of continuous control over the entire frequency range,

while in most Implementations of crisp adaptive PLLs, at leastone range of control is discrete.


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stepsinvolved in Fuzzy control phase locked

loop:

y Fuzzification,

y Knowledge representation,

y Inferences,

y Defuzzification.


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FUZZIFICATION:y The first step is the fuzzification of input and output variables

after carrying out experimental observations. Phase difference(*) is selected as input variable and the output, Vdc is outputvariable. These two variables are fuzzified over their practical

domains as shown in fig.


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KNOWLEDGE REPRESENTATION:

y knowledge representation consists of rule base and database

y Database: This module provides information like domains;

membership functions for input parameters * and vd.

y Rule Base: The following rules have been formulated to

optimize the phase locking process. The membership functionswere tuned to decide the weightage of each rule.


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FUZZY INFERENCE:

y Ours is the single input single output (SI-SO) System.

y In the process of inference, each rule is individually fired by crisp

value of phase angle.

y This in turn generates clipped fuzzy sets (CFS).


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y Database for* and VdcInput data

yQAR0 (*)=L (*, 0,45)

y QAR45 (*)=A (*, 0,45,90)

y QAR90 (*)=A (*, 45,90,135)

y QAR135 (*)=A (*, 90,135,180)

y

QAR135 (*)=+(*,135,180).output data

y QVL(Vdc)=L(Vdc,4.4,6.2)

y QMH(Vdc)=A(V-dc,8.12,10.18,12.0)

y QH(Vdc

)=(Vdc

,10.18,122)

Fuzzy if then rules

Rule 1: if* is AR

(0), then Vdc is HRule 2: if* is AR

(45), then Vdc is MH

Rule 3: if* is

AR(90), then Vdc

MLRule 4: if* is

AR(135), then Vdc is

L

Rule 5 f* is

AR(180), then Vdc isVL


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DEFUZZIFICATION

y This is last step in the implementation of output of FCPLL.

y This gives compromised decision regarding the dc voltage.

y Defuzzification converts overall fuzzy output of fuzzy inference

into crisp value that corresponds to exact value of dc voltage.


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FREQUENCY LOCK IN RANGE OF FUZZY

CONTROLLED PLL:

y Step 1: Define inputs and outputs for the FC-PLL

y Step 2: fuzzy the inputs:

y Step 3: setup fuzzy membership function for outputs.

y Step 4: create a fuzzy rule base:


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AREAS OF APPLICATION:

y Fuzzy logic approach to direct phase control converter DC

machine drive.

y Fuzzy control for output current phase controlled rectifier.

y

Application of fuzzy logic in the phase locked speed control ofinduction motors.

y Digital loop present synthesizer (DLPS) for high-speed frequencyswitching.

y Design of a control system implementing fuzzy logic inprogrammable switching.


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Conclusion

The Characteristics of PLL (IC 565) were studied infrequency range 1.2kHz-1.52kHz for classic PLL.

The said PLL showed the lesser signal to noise ratio and

larger lock-in-range. signal to With introduction of Fuzzy controller at

appropriate noise ratio improved by 13dB and lock-in-rangeof frequency is reduced by 10%.

Thus, Fuzzy logic PLL performs better than the said analogclassic PLL.

.


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QUERRIES ?????.


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THANKYOU

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Space Robotics 470