Fuzzy Pre-compensated PI Controller for PMBLDC Motor Drive
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Transcript of Fuzzy Pre-compensated PI Controller for PMBLDC Motor Drive
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Fuzzy Pre-compensated PI Controller forPMBLDC Motor Drive
- The advanced digitalized control of
DC motor
Seminar presentation by-
PAPANNA DHANANJAY,
M.TECH-POWER ELECTRONICS,
1st SEMESTER, TOCE, EEE
nder The Guidance,r. JAY KUMAR,ENIOR LECTURER,OCE, EEE Dept, VTU.
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22Fuzzy Pre-compensated PI
INTRODUCTION:
Fuzzy Pre-compensated PI Controller is the advanced speed
control method forPMBLDC (Permanent Magnet Brushless
Direct Current) Motor Drive which provides precise
mathematical modeling of the system feasibly.
Its most cost competitive and eliminate hardware &
software design complexity in the system.
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33Fuzzy Pre-compensated PI
AIM:
esign a PMBLDC motor drive that satisfies the following needs:
od dynamic performance with zero steady state error.
curate mathematical model of the drive system for tuning tI gains in order to overcome perturbation and variations ofystem parameters.
duce the hardware & software design complexity.
duce chatter, energy loss, plant damage, and excitation ofnmodelled dynamics.
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44Fuzzy Pre-compensated PI
Existing System:
PMBLDC MOTOR DRIVECONTROLLERS
Sliding ModeController
PI /PID controller
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PROBLEM STATEMENT:
Fuzzy Pre-compensated PI
nventional PMBLDC motor drive controllers we are facingollowing problems:
terred dynamic performance with zero steady state error.
e variation of the operating point of the system, such asvariation of reference speed, load and the DC linkvoltage which leads to oscillations in the system.
atter, energy loss, plant damage, and excitation ofunmodelled dynamics.
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66Fuzzy Pre-compensated PI
The Challenge:
To design the universally acceptable PMBLDCmotor drive controller
that satisfies all the aims and overcomes all the problems specified.
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77Fuzzy Pre-compensated PI
PROPOSED SOLUTION:
Use Fuzzy Logic technique to design the PMBLDC motor drive
controller.
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88Fuzzy Pre-compensated PI
FUZZY LOGIC:
Fuzzy Logic incorporates a simple, rule-based
IF X AND Y THEN Z
approach to a solving control problem rather than attempting to model a
system mathematically.For example: Let us consider motor speed control
Instead of "SP =500HP
We take "T
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Programming methodology:
"IF (process is too slow) AND (process is getting slower)
THEN (add speed to the process)"
or
"IF (process is too fast) AND (process is speeding rapidly)
THEN (slow the process quickly)
Fuzzy Pre-compensated PI(a) Binary logic (b) Fuzzy logic
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PI CONTROLLER:
PI Controller (proportional-integral controller)
is a feedback controller which drives the plant to be controlled with a
weighted sum of the error.
SP: Set Point
PV: Process Variable
MV: Manipulated Variable
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Advantage of a Proportional Integral
Controller:
PI controller causes the steady-state error to be zerofor a step input.
Disadvantages of a Proportional Plus Integral Controller:PI controller introduces a phase-lag. This means that the phase
margin (a measure of stability) decreases.
So careful design considerations with respect to the gain must be
considered.
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Digital Signal Processing is concerned with the
representation of the signals by a sequence of numbers or
symbols and the processing of these signals. Digital signal processing
and analog signal processing are subfields of signal processing.
DSP includes subfields like: sensor array processing, audio signal
processing, spectral estimation, statistical signal processing, etc.
DSP (Digital Signal Processing):
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1313 Fuzzy Pre-compensated PI Controllerfor PMBLDC MOTORBLOCK DIAGRAM-I:
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1. The Power circuit
2. The controller
q Reference speed
q Rotor speed monitoring
q The speed controller
q Rotor position sensingq Reference current generation
q Winding current sensing
q
The current controllerq Gate drive control unit
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BLOCK DIAGRAM-IIWORKING OF FUZZY PRECOMPENSATED PI SPEED CONTROLLER
The Speed Controller
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I. The Speed Controller
e (n)= w* (n) w(n) ) e (n) => speed error of electric drive.
y(n) = f [e (n), e (n)] w* => reference speed of electric drive
e(n) = e(n)-e(n-1) w => actual speed of electric drive.
w*'= w* + y(n) e (n) => change in error
e'(n) = w*' w y (n) is a compensation term
e' (n) = e' (n) - e' (n-1) (determined on the basis of fuzzy logicTref (n) = Tref (n-1) + mapping function) at an instant n.
Kp e' (n) +Ki e' (n) f [e (n), e (n)] => compensation term
II.
Fuzzy Pre-compensator w*' => compensated reference signalq. Structure of Fuzzy Logic Tref => torque reference
Control
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Fuzzy Process consists of the following five steps:
Fuzzification of input variable:
The process of fuzzification is assigning membership functions forvarious fuzzy subsets (seven fuzzy subsets namely PB, PM, PS,
ZE, NS, NM, NB are considered in this study) on the basis of I/P.
Application of Fuzzy operator (AND, OR, NOT ) in theIF part of the rule.
IF e (n) is negative big (NB) AND e (n-1) is positive big (PB)
IF is negative big (NB) AND is Zero (ZE)
Fuzzy Pre-compensated PI Controllerfor PMBLDC MOTOR
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3. Implication from the antecedent to the consequent (THEN
part of the rule)
THEN u is Zero (ZE).THEN u is "negative medium (NM)".
4. Aggregation of the consequent across the rules.
With the seven fuzzy subsets forty-nine rules are writtenThese rules are given in Table-2.
For example: IF =+1 AND =-2 THEN u =-1
Fuzzy Pre-compensated PI Controllerfor PMBLDC MOTOR
TABLE II
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TABLE-II
Inference: The inference is the process of determining the degree of
belonging of the output (u) to the various fuzzy subsets,
for given values of and .
Th d f b l i f () d h i () t f
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The degree of belonging of error () and change in error () to fuzzy
subsets is shown in Table. The degree of belonging for output (u) is als
calculated in this table.
TABLE: Inference
DEFUZZIFICATION
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4. DEFUZZIFICATION:
Defuzzification is the process of obtaining crisp output from
the output of the interference and fuzzy subsets. To obtain u,
the following equation is used-
Where,u=> output fuzzy setAi => output fuzzy subsetai => degree of belonging (output of interference)
Example:u= -2*0.18-1*0.42-1*0.12-1*0.28 = 1.180.18+0.42+0.12+0.28
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Basic structure of fuzzy logic control:
Fuzzy membership function
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The different output fuzzy sets:
output negative, output zero,output positive
The input fuzzy sets havingPositive and negative error
DSP B d I l t ti f th D i S t
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DSP Based Implementation of the Drive System:
Finally the assembly language software is developed for closed loop
speed control of the PMBLDC drive system with Fuzzy
Pre-compensated PI speed
Controller technique.
Here TMS320F240 DSP
microchip is used.
RESULTS AND DISCUSSION
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RESULTS AND DISCUSSION:
(PI speed controller) (Fuzzy pre-compensated PI controller)Scale: 300 rpm /div (channel 1 and 2), 4 A/div (channel 3 and 4)
100-ms/ div. (time scale)Figure: Starting response of the PMBLDC motor drive.Let the drive speed be 500rpm (reference speed). starting response ofFuzzy precompensated PI speed controller the starting time is 20 msecless than the PI speed controller.
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(PI speed controller) (Fuzzy pre-compensated PI controller)Scale: 300 rpm /div (channel 1 and 2), 4 A/div (channel 3 and 4)
100-ms/ div. (time scale)
Figure: Speed reversal response of the PMBLDC motor drive.
Let the drive speed be -500rpm (reference speed). Reversal response
time is 700 ms with fuzzy precompensated PI and 900 ms with PI speed
controllers. Hence, there is reduction in reversal time by 200 ms.
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SUMMARY OFWORK
In case of permanent magnet motor, Fuzzy logic controlapparently offers a possibility of obtaining an improvement in thequality of the speed response, compared to PI control.
Fuzzy logic provides Accurate mathematical model ofthe drive system for tuning the PI gains in order toovercome perturbation and variations of systemparameters.
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Finally, I Thank to novel fuzzy logic method
That as boosted the performance of the control system in
electric drives.
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Q/A
Questions and answer time
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THANK YOU