Jain, Saurabh

Simon, Daniel J.

English

Cleveland-Marshall College of Law

Cleveland State UniversityEngagedScholarship@CSUElectrical Engineering & Computer Science FacultyPublicationsElectrical Engineering & Computer ScienceDepartment9-2004Controller Tuning for DC Motor Speed ControlUsing Genetic AlgorithmsSaurabh JainCleveland State University, s.jain1@csuohio.eduDaniel J. SimonCleveland State University, d.j.simon@csuohio.eduFollow this and additional works at: https://engagedscholarship.csuohio.edu/enece_facpubPart of the Electrical and Computer Engineering CommonsHow does access to this work benefit you? Let us know!Repository CitationJain, Saurabh and Simon, Daniel J., "Controller Tuning for DC Motor Speed Control Using Genetic Algorithms" (2004). Electrical Engineering &Computer Science Faculty Publications. 201.https://engagedscholarship.csuohio.edu/enece_facpub/201This Presentation is brought to you for free and open access by the Electrical Engineering & Computer Science Department atEngagedScholarship@CSU. It has been accepted for inclusion in Electrical Engineering & Computer Science Faculty Publications by an authorizedadministrator of EngagedScholarship@CSU. For more information, please contact library.es@csuohio.edu.Original CitationS. Jain and D. Simon, “Controller Tuning for DC Motor Speed Control Using Genetic Algorithms,” Aerospace Power and ElectronicsSimulation Workshop, Cleveland, OH, September 2004Aerospace Power & Electronics Simulation Workshop 2004Controller Tuning for DC Motor Speed Control Using Genetic AlgorithmsSaurabh Jain Dan SimonSeptember 21, 20042Aerospace Power & Electronics Simulation Workshop 2004Presentation Overview? Purpose of research? Solution strategy? Introduction to Genetic Algorithms (GA’s)? Concept of GAs? Basic Algorithm? Representation? Simplorer and Matlab implementation? Conclusions and future work3Aerospace Power & Electronics Simulation Workshop 2004Purpose of research? To investigate a new method (Diploid GA) of motor control? To use Simplorer to evaluate design performance? To consider a broad optimization function for parameter evaluation4Aerospace Power & Electronics Simulation Workshop 2004Problem Statement? Speed Control of DC Motors? Controller tuning (finding Kp & Ki)? Aerospace Applications? Flywheel energy storage? Flight control trim surfaces? Hydraulics? Fans? Thrust vector control? Fuel pumps5Aerospace Power & Electronics Simulation Workshop 2004Presentation Overview? Purpose of research? Solution strategy? Introduction to Genetic Algorithms (GA’s)? Concept of GAs? Basic Algorithm? Representation? Simplorer and Matlab implementation? Conclusions and future work6Aerospace Power & Electronics Simulation Workshop 2004Solution StrategySpeedSpeedMotion Profile7Aerospace Power & Electronics Simulation Workshop 2004Optimization function IMinimize Overshoots8Aerospace Power & Electronics Simulation Workshop 2004Optimization function II1 1 2 3 41 2max ( ) ( )s t a m m m ms tc s tcF w i t k O k O k O k O= + + + +( ( ), ( ))s mF i t O tφ=min ormax 1/s sF Fis the maximum overshootis the rotor currentare the weighting factorsikmOai9Aerospace Power & Electronics Simulation Workshop 2004Presentation Overview? Purpose of research? Solution strategy? Introduction to Genetic Algorithms (GA’s)? Concept of GAs? Basic Algorithm? Representation? Simplorer and Matlab implementation? Conclusions and future work10Aerospace Power & Electronics Simulation Workshop 2004Introduction to GAs? Genetic algorithms (GA’s) are a technique to solve problems which need optimization? GA’s are a subclass of Evolutionary Computing? GA’s are based on Darwin’stheory of evolution11Aerospace Power & Electronics Simulation Workshop 2004Concept of GAs I? Most often one is looking for the best solution in a specific subset of solutions? This subset is called the search space (or state space)? Every point in the search space is a possible solution? Therefore every point has a fitness value, depending on the problem definition? GA’s are used to search thesearch space for the bestsolution, e.g. a minimum? Difficulties are the local minima and the startingpoint of the search0 100 200 300 400 500 600 700 800 900 100000.511.522.512Aerospace Power & Electronics Simulation Workshop 2004Concept of GAs II? Starting with a subset of n randomly chosen solutions from the search space (i.e. chromosomes). This is the population? This population is used to produce a next generation of individuals by reproduction? Individuals with higher fitness have more chance to reproduce (i.e. natural selection)13Aerospace Power & Electronics Simulation Workshop 2004The Basic Algorithm0  START : Create random population of nchromosomes1  FITNESS : Evaluate fitness f(x) of each chromosome in the population2  NEW POPULATION0 SELECTION     : Based on f(x)1 RECOMBINATION : Cross-over chromosomes2 MUTATION      : Mutate chromosomes3 ACCEPTATION   : Reject or accept new one3  REPLACE : Replace old with new population: the new generation4  TEST    : Test problem criterium5  LOOP    : Continue step 1 – 4 until criterium is satisfied14Aerospace Power & Electronics Simulation Workshop 2004Representation I? Genetic information is stored in the chromosomes? Each chromosome is build of DNA? The chromosome is divided in parts: genes? Genes code for properties? The posibilities of the genes for one property is called: allele? Every gene has an unique position on the chromosome: locus15Aerospace Power & Electronics Simulation Workshop 2004Types of GAsHaploid GAs?Easy to use?Less ComputationDiploid GAs?Good for Dynamic Environments?More Diversity16Aerospace Power & Electronics Simulation Workshop 2004Representation II? The entire combination of genes is called genotype? A genotype develops to a phenotype? Alleles can be either dominant or recessive? Dominant alleles will always express from the genotype to the phenotype? Recessive alleles can survive in the population for many generations, without being expressed17Aerospace Power & Electronics Simulation Workshop 2004Cell Division18Aerospace Power & Electronics Simulation Workshop 2004Genetic OperatorsMeiosisSelectionNext PopulationMutation19Aerospace Power & Electronics Simulation Workshop 2004Haploid Reproduction3.73.94.3Haploid adultsfusionmeiosis“crossover” P’diploid“zygote”haploidchildHAPLOID20Aerospace Power & Electronics Simulation Workshop 2004Diploid Reproduction21Aerospace Power & Electronics Simulation Workshop 2004Presentation Overview? Purpose of research? Solution strategy? Introduction to Genetic Algorithms (GA’s)? Concept of GAs? Basic Algorithm? Representation? Simplorer and Matlab implementation? Conclusions and future work22Aerospace Power & Electronics Simulation Workshop 2004Simplorer and Matlab implementation: Matlab Processing23Aerospace Power & Electronics Simulation Workshop 2004Simplorer and Matlab implementation: Simplorer Processing24Aerospace Power & Electronics Simulation Workshop 2004Simplorer ModelxnPOWCONSTDivisorMULCONSTSet_Point_Speed_rpmNEG GAINP_ControlII_Control ControllerLIMITNEGControl_OutGAINMeasured_IampsGAINMeasured_HzMMOS1MOS2MOS3MOS4D1D2D3D4EDCMOTORD5NEGSiM2SiMSIMPLORER   Link Interfacei iSIM2SIM1P := PI := IOVERSHOOTINTEGRALOVERSHOOTINTEGRALCount1_ResetCount1_EnablePI_SET_STATETRANS2EQUBLLOAD_TORQUETRANS3TRANS4TRANS5 TRANS6EQUBLTRIGGERTRANS1TERMINATION_STATEMEASURECONDITIONCONSTCONST1EQUBLTRIGGER1EVENTIgainPgainIgainPgainCONDITION.VAL := CONDITION.VALTRIGGER.VAL[0] := TRIGGER.VAL[0]IALOADGAINGAIN1DDIFFCALCe:=m c²PROBE21CONSTCONST2CONSTCONST3CALCe:=m c²PROBE22EVENT1CONSTIMAXCONSTRIMAXCONSTCONST4IAETF := TFOVERSHOOTOVERSHOOT1OVERSHOOT OVERSHOOT2OVERSHOOTOVERSHOOT3EQUBLO_SUMCLRQ2Q3Q1Q0BinaryCLKCLK   AND2Q=>BSCLRQ2Q3Q1Q0BinaryCLKDACVAL[3:0]INPUTRESET_CLKDAC1CONSTAND2B1DACVAL[3:0]INPUTDAC2Q=>BSQ=>BSGAINGAINCount3_ResetCount3_EnableCount4_ResetCount4_Ebable Simulink_TriggerLoad_TorqueTRANS9Load_TorqueTRANS10CLRQ2Q3Q1Q0BinaryDACVAL[3:0]INPUTDAC3CLRQ2Q3Q1Q0BinaryDACVAL[3:0]INPUTDAC4GAINGAIN Q=>BSQ=>BS   AND2   AND2CLKQ=>BSCLKQ=>BSGAINGAINEQUTIMERST := ST25Aerospace Power & Electronics Simulation Workshop 2004Functional Blocks26Aerospace Power & Electronics Simulation Workshop 2004State Flow27Aerospace Power & Electronics Simulation Workshop 2004Simulink Model28Aerospace Power & Electronics Simulation Workshop 2004Simulation ResultsPlot of Population Average Fitness and Maximum fitness in each generation vs Number of GenerationsParametersPopulation Size 200Max Generations 150Mean fitness29Aerospace Power & Electronics Simulation Workshop 2004Simulation ResultsResponse30Aerospace Power & Electronics Simulation Workshop 2004Simulations Results31Aerospace Power & Electronics Simulation Workshop 2004Simulation Results32Aerospace Power & Electronics Simulation Workshop 2004Conclusions and Future work? Considering the optimization criteria we have achieved satisfactory results. Longer GA runs are expected to perform even better? This can be expanded to encompass more parameters from the system? Real time implementation (on-line tuning)? Comparison with other tuning methods / haploid GA’s

Controller Tuning for DC Motor Speed Control Using Genetic Algorithms

EngagedScholarship @ Cleveland State University

Cleveland State University
EngagedScholarship@CSU
Electrical Engineering & Computer Science Faculty
Publications
Electrical Engineering & Computer Science
Department
9-2004
Controller Tuning for DC Motor Speed Control
Using Genetic Algorithms
Saurabh Jain
Cleveland State University, s.jain1@csuohio.edu
Daniel J. Simon
Cleveland State University, d.j.simon@csuohio.edu
Follow this and additional works at: https://engagedscholarship.csuohio.edu/enece_facpub
Part of the Electrical and Computer Engineering Commons
How does access to this work benefit you? Let us know!
Repository Citation
Jain, Saurabh and Simon, Daniel J., "Controller Tuning for DC Motor Speed Control Using Genetic Algorithms" (2004). Electrical Engineering &
Computer Science Faculty Publications. 201.
https://engagedscholarship.csuohio.edu/enece_facpub/201
This Presentation is brought to you for free and open access by the Electrical Engineering & Computer Science Department at
EngagedScholarship@CSU. It has been accepted for inclusion in Electrical Engineering & Computer Science Faculty Publications by an authorized
administrator of EngagedScholarship@CSU. For more information, please contact library.es@csuohio.edu.
Original Citation
S. Jain and D. Simon, “Controller Tuning for DC Motor Speed Control Using Genetic Algorithms,” Aerospace Power and Electronics
Simulation Workshop, Cleveland, OH, September 2004
Aerospace Power & Electronics Simulation Workshop 2004
Controller Tuning for 
DC Motor Speed Control Using 
Genetic Algorithms
Saurabh Jain 
Dan Simon
September 21, 2004
2Aerospace Power & Electronics Simulation Workshop 2004
Presentation Overview
? Purpose of research
? Solution strategy
? Introduction to Genetic Algorithms (GA’s)
? Concept of GAs
? Basic Algorithm
? Representation
? Simplorer and Matlab implementation
? Conclusions and future work
3Aerospace Power & Electronics Simulation Workshop 2004
Purpose of research
? To investigate a new method (Diploid GA) 
of motor control
? To use Simplorer to evaluate design 
performance
? To consider a broad optimization function 
for parameter evaluation
4Aerospace Power & Electronics Simulation Workshop 2004
Problem Statement
? Speed Control of DC Motors
? Controller tuning (finding Kp & Ki)
? Aerospace Applications
? Flywheel energy storage
? Flight control trim surfaces
? Hydraulics
? Fans
? Thrust vector control
? Fuel pumps
5Aerospace Power & Electronics Simulation Workshop 2004
Presentation Overview
? Purpose of research
? Solution strategy
? Introduction to Genetic Algorithms (GA’s)
? Concept of GAs
? Basic Algorithm
? Representation
? Simplorer and Matlab implementation
? Conclusions and future work
6Aerospace Power & Electronics Simulation Workshop 2004
Solution Strategy
S
p
e
e
d
S
p
e
e
d
Motion Profile
7Aerospace Power & Electronics Simulation Workshop 2004
Optimization function I
Minimize Overshoots
8Aerospace Power & Electronics Simulation Workshop 2004
Optimization function II
1 1 2 3 41 2
max ( ) ( )s t a m m m ms tc s tcF w i t k O k O k O k O= + + + +
( ( ), ( ))s mF i t O tφ=
min ormax 1/s sF F
is the maximum overshoot
is the rotor current
are the weighting factorsik
mO
ai
9Aerospace Power & Electronics Simulation Workshop 2004
Presentation Overview
? Purpose of research
? Solution strategy
? Introduction to Genetic Algorithms (GA’s)
? Concept of GAs
? Basic Algorithm
? Representation
? Simplorer and Matlab implementation
? Conclusions and future work
10Aerospace Power & Electronics Simulation Workshop 2004
Introduction to GAs
? Genetic algorithms (GA’s) are a technique to 
solve problems which need optimization
? GA’s are a subclass of Evolutionary 
Computing
? GA’s are based on Darwin’s
theory of evolution
11Aerospace Power & Electronics Simulation Workshop 2004
Concept of GAs I
? Most often one is looking for the best solution in a specific subset 
of solutions
? This subset is called the search space (or state space)
? Every point in the search space is a possible solution
? Therefore every point has a fitness value, depending on the 
problem definition
? GA’s are used to search the
search space for the best
solution, e.g. a minimum
? Difficulties are the local 
minima and the starting
point of the search
0 100 200 300 400 500 600 700 800 900 1000
0
0.5
1
1.5
2
2.5
12Aerospace Power & Electronics Simulation Workshop 2004
Concept of GAs II
? Starting with a subset of n randomly chosen solutions 
from the search space (i.e. chromosomes). This is the 
population
? This population is used to produce a next generation of 
individuals by reproduction
? Individuals with higher fitness have more chance to 
reproduce (i.e. natural selection)
13Aerospace Power & Electronics Simulation Workshop 2004
The Basic Algorithm
0  START : Create random population of n
chromosomes
1  FITNESS : Evaluate fitness f(x) of each 
chromosome in the population
2  NEW POPULATION
0 SELECTION     : Based on f(x)
1 RECOMBINATION : Cross-over chromosomes
2 MUTATION      : Mutate chromosomes
3 ACCEPTATION   : Reject or accept new one
3  REPLACE : Replace old with new population: the 
new generation
4  TEST    : Test problem criterium
5  LOOP    : Continue step 1 – 4 until criterium is 
satisfied
14Aerospace Power & Electronics Simulation Workshop 2004
Representation I
? Genetic information is stored in 
the chromosomes
? Each chromosome is build of 
DNA
? The chromosome is divided in 
parts: genes
? Genes code for properties
? The posibilities of the genes for 
one property is called: allele
? Every gene has an unique 
position on the chromosome: 
locus
15Aerospace Power & Electronics Simulation Workshop 2004
Types of GAs
Haploid GAs
?Easy to use
?Less Computation
Diploid GAs
?Good for Dynamic 
Environments
?More Diversity
16Aerospace Power & Electronics Simulation Workshop 2004
Representation II
? The entire combination of genes 
is called genotype
? A genotype develops to a 
phenotype
? Alleles can be either dominant 
or recessive
? Dominant alleles will always 
express from the genotype to 
the phenotype
? Recessive alleles can survive in 
the population for many 
generations, without being 
expressed
17Aerospace Power & Electronics Simulation Workshop 2004
Cell Division
18Aerospace Power & Electronics Simulation Workshop 2004
Genetic Operators
Meiosis
Selection
Next Population
Mutation
19Aerospace Power & Electronics Simulation Workshop 2004
Haploid Reproduction
3.7
3.9
4.3
Haploid 
adults
fusion
meiosis
“crossover” P’
diploid
“zygote”
haploid
child
H
A
P
L
O
I
D
20Aerospace Power & Electronics Simulation Workshop 2004
Diploid Reproduction
21Aerospace Power & Electronics Simulation Workshop 2004
Presentation Overview
? Purpose of research
? Solution strategy
? Introduction to Genetic Algorithms (GA’s)
? Concept of GAs
? Basic Algorithm
? Representation
? Simplorer and Matlab implementation
? Conclusions and future work
22Aerospace Power & Electronics Simulation Workshop 2004
Simplorer and Matlab 
implementation: Matlab Processing
23Aerospace Power & Electronics Simulation Workshop 2004
Simplorer and Matlab 
implementation: Simplorer Processing
24Aerospace Power & Electronics Simulation Workshop 2004
Simplorer Model
xn
POW
CONSTDivisor
MUL
CONSTSet_Point_Speed_rpm
NEG GAIN
P_Control
I
I_Control Controller
LIMIT
NEG
Control_Out
GAIN
Measured_Iamps
GAINMeasured_Hz
M
MOS1
MOS2
MOS3
MOS4
D1
D2
D3
D4
E
DCMOTOR
D5
N
E
G
SiM2SiM
SIMPLORER   Link Interface
i i
SIM2SIM1
P := P
I := I
OVERSHOOT
INTEGRAL
OVERSHOOT
INTEGRAL
Count1_Reset
Count1_Enable
PI_SET_STATE
TRANS2
EQUBL
LOAD_TORQUE
TRANS3
TRANS4
TRANS5 TRANS6
EQUBL
TRIGGER
TRANS1
TERMINATION_STATE
MEASURE
CONDITION
CONST
CONST1
EQUBL
TRIGGER1
EVENT
Igain
Pgain
Igain
Pgain
CONDITION.VAL := CONDITION.VAL
TRIGGER.VAL[0] := TRIGGER.VAL[0]
IA
LOAD
GAIN
GAIN1
D
DIFF
CALC
e:=m c²
PROBE21
CONST
CONST2
CONST
CONST3
CALC
e:=m c²
PROBE22EVENT1
CONST
IMAX
CONST
RIMAX
CONST
CONST4
IAE
TF := TF
OVERSHOOT
OVERSHOOT1
OVERSHOOT OVERSHOOT2
OVERSHOOT
OVERSHOOT3
EQUBL
O_SUM
CLR
Q2
Q3
Q1
Q0
Binary
C
L
K
C
L
K
   AND2
Q=>BS
CLR
Q2
Q3
Q1
Q0
Binary
C
L
K
DAC
VAL
[3:0]
INPUT
RESET_CLK
DAC1
CONST
AND2B1
DAC
VAL
[3:0]
INPUT
DAC2
Q=>BS
Q=>BS
G
A
I
N
G
A
I
N
Count3_Reset
Count3_Enable
Count4_Reset
Count4_Ebable Simulink_Trigger
Load_Torque
TRANS9
Load_Torque
TRANS10
CLR
Q2
Q3
Q1
Q0
Binary
DAC
VAL
[3:0]
INPUT
DAC3
CLR
Q2
Q3
Q1
Q0
Binary
DAC
VAL
[3:0]
INPUT
DAC4
GAIN
GAIN Q=>BS
Q=>BS
   AND2
   AND2
CLK
Q=>BS
CLK
Q=>BS
GAIN
GAIN
EQU
TIMER
ST := ST
25Aerospace Power & Electronics Simulation Workshop 2004
Functional Blocks
26Aerospace Power & Electronics Simulation Workshop 2004
State Flow
27Aerospace Power & Electronics Simulation Workshop 2004
Simulink Model
28Aerospace Power & Electronics Simulation Workshop 2004
Simulation Results
Plot of Population Average Fitness and Maximum fitness in each 
generation vs Number of Generations
Parameters
Population Size 200
Max Generations 150
M
e
a
n
 
f
i
t
n
e
s
s
29Aerospace Power & Electronics Simulation Workshop 2004
Simulation Results
R
e
s
p
o
n
s
e
30Aerospace Power & Electronics Simulation Workshop 2004
Simulations Results
31Aerospace Power & Electronics Simulation Workshop 2004
Simulation Results
32Aerospace Power & Electronics Simulation Workshop 2004
Conclusions and Future 
work
? Considering the optimization criteria we 
have achieved satisfactory results. Longer 
GA runs are expected to perform even 
better
? This can be expanded to encompass more 
parameters from the system
? Real time implementation (on-line tuning)
? Comparison with other tuning methods / 
haploid GA’s


https://engagedscholarship.csuohio.edu/cgi/viewcontent.cgi?article=1206&amp;context=enece_facpub

Controller Tuning for DC Motor Speed Control Using Genetic Algorithms

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