probral kl july2007 - engenharia elétrica · projects: sapien , ct -energ and finep -inova energy...

International Research CooperationPROBRAL 2007-2008 CAPES/DAAD

University of Brasília/Brazil – University of Kaiserslautern/Germany

“Networked Control with Distributed Processing for Building Automation

in an Ambient Intelligence Framework”

Adolfo BauchspiessAdolfo Bauchspiess

KaiserslauternKaiserslautern, 23.7.2006, 23.7.2006

CAPES/DAAD PROBRAL UnB/KL CAPES/DAAD PROBRAL UnB/KL ®®A.Bauchspiess 23.7.2007A.Bauchspiess 23.7.2007

ContentsContents

�� Brazil Brazil -- BrasBrasíília lia –– UnBUnB

�� PROBRALPROBRAL

�� Some on going UnBSome on going UnB--ProjectsProjects

�� Energy Saving with Fuzzy Distributed ControlEnergy Saving with Fuzzy Distributed Control

�� Wire Less Building AutomationWire Less Building Automation

�� Conclusions & PerspectivesConclusions & Perspectives


BRAZILBRAZIL•• PelPeléé -- SoccerSoccer•• CarnavalCarnaval –– SambaSamba•• Rio de JaneiroRio de Janeiro


BrazilBrazil

Brasília15°45´S47°52´W

~ 8.5 Mio Km2

~ 190 Mio Inh.~ 8.000Km Coast

BRInC

Equator


BrasBrasíílialia –– Capital City of Capital City of BrazilBrazil• 1960 – President J. Kubitschek, Architekt O. Niemeyer • 1.000m over the see level• ~ 2 Million Inhabitants (Federal District)• Highest income rate in south america• Paranoá Lake


UniversidadeUniversidade de de BrasBrasíílialia(Oscar Niemeyer, 1961)(Oscar Niemeyer, 1961)

1Km – Instituto Central de Ciências

FT


TechnologicalTechnological FacultyFaculty

4 4 DepartmentsDepartments•• ElectricalElectricalEng. Eng. -- ~200 ~200 Beginners/YearBeginners/Year•• MechanicalMechanicalEng.Eng.•• Civil Eng. Civil Eng. •• Forest Eng.Forest Eng.

6 6 DegreesDegrees••ElectricalElectricalEng.Eng.••MechatronicMechatronicEng.Eng.••CommunicationCommunicationNetworkNetworkEng.Eng.••MechanicalMechanicalEng.Eng.••Civil Eng.Civil Eng.••Forest Eng.Forest Eng.


ElectricalElectrical EngineeringEngineering DepartmentDepartment

GRAVGRAVAssociateAssociateProf. Dr. Adolfo Bauchspiess Prof. Dr. Adolfo Bauchspiess AdjointAdjoint Prof. Dr. Prof. Dr. GeovanyGeovanyde Arade Araúújo Borgesjo BorgesAdjointAdjoint Prof. Dr. João Prof. Dr. João YoshiyukiYoshiyukiIshiharaIshiharaAdjointAdjoint Prof. Dr. Marco A. F. Egito CoelhoProf. Dr. Marco A. F. Egito Coelho

Areas organized in research groups,GRAV – Grupo de Robótica, Automação e Visão Computacional

LAVSI – Laboratório de Automação, Visão e Sistemas InteligentesLARA – Laboratório de Robótica e Automação

43+ 43+ lecturerslecturersin 5 in 5 areasareas: : ControlControlandandAutomationAutomation, , TelecomTelecom, , ElectronicsElectronics, , PowerPowerSystemsSystems&& CommunicationCommunicationNetworksNetworks..


PROBRALPROBRALPROPROgrama de Cooperagrama de Cooperaççãoão Internacional Internacional

BRBRasilasil--ALALemanhaemanha

�� Only supports Interchange of ResearchersOnly supports Interchange of Researchers

�� Each side must have local research ($) supportEach side must have local research ($) support

�� ~ 2 Profs. + 2 Ph.D candidates / Year~ 2 Profs. + 2 Ph.D candidates / Year

�� 2 (+ 1) Years2 (+ 1) Years

“Networked Controlwith Distributed Processingfor Building Automation

in an Ambient IntelligenceFramework”


Some onSome on--going UnBgoing UnB--ProjectsProjects

�� LEARnLEARn

�� ExpansionExpansion--CARCARAHCARCARAH

�� ......

�� Related with PROBRAL:Related with PROBRAL:

•• SAPIEnSAPIEn

•• CTCT--EnergEnerg

•• FINEPFINEP--INOVAINOVA


LEARnLEARnLaboratório de Ensino de Automação Remota.


UAV Inspection of Power UAV Inspection of Power Transmission LinesTransmission Lines

ProjectProject CarcarahCarcarah

--Expansion Expansion LtdaLtda

--ANEELANEEL


Projects: Projects: SAPIEnSAPIEn, CT, CT--EnergEnergand FINEPand FINEP--INOVAINOVA

EnergyEnergy--Saving Approach:Saving Approach:

ModelModel--Based HVAC ControlBased HVAC Control

( )

444 3444 21444 3444 21relatedenergyrelatedcomfort

h

iu

Tu

Th

iR

cp

ikikikikyikyJ ∑∑−

=∆

=

++++∆+∆+−+=1

01

2 )()()()()( uQuuQu

Predictive Cost Function:Predictive Cost Function:

matricesweighting

iablevardmanipulateu

referencey

iablevarcontrolledy

horizoncontrolh

horizonpredictionh

Where

uu

R

c

p

−−

−−−

−

∆ QQ ,

:

Considers comfort and energy saving. Needs model!Considers comfort and energy saving. Needs model!


DistributedDistributed ParameterParameter ModellingModelling

�� Transport PhenomenaTransport Phenomena

�� Ex. Heat FlowEx. Heat Flow

Wind velocity simulation in Modelica, by Felgner, ASIM2002


DynamicDynamic SystemSystem IdentificationIdentification

u - input (manipulated variable)y – output (controlled variable)w – measurable disturbancev – non-measurable disturbance


FirstFirst--Principles Identification (KLPrinciples Identification (KL--2006)2006)

15 20 25 30 35 40 45

0

5

10

15

20

t/hours

Identification data set of March 29-31, 2006

h on/off

-c on/off

Gs/50 W/m2Tw/°C

To/°C

Typical identification data set


a) Linear MISO approacha) Linear MISO approach

0 105

0

5

10

15

20

25Heat

(sec)

0 5 10 155

-300

-250

-200

-150

-100

-50

0Cool

(sec)

0 5 10 155

0

0.2

0.4

0.6

0.8

1

1.2

1.4Outside Temp

(sec)

x 105x 105 x 105

12 14 16 18 20 22 24 26 28 30 3217

18

19

20

21

22

23

24

t/hours

u=[Th Tc Tw Tv Gs] 6th order MISO model

To

y

DuCx

EeBuAxx

+=++=

y

&

-1 0 1

-1

-0.5

0

0.5

1

From Th

-1 0 1

-1

0

1

From Tc

-1 0 1

-1

0

1

From Tw


b) b) FirstFirst--Principles Structured Principles Structured IdentificationIdentification

2R1C analogy of the vicinity heat transfer 2R1C-based disturbance model

)1)(1(

)(.

)1)(1(

)(.

)1)(1(

)(.

+++

+++

++=

saas

sTsaK

saas

sTsaK

saas

sGsaKq

w

ww

v

vv

r

srdHeat flow due to disturbances:


FirstFirst--principles structured thermal modelprinciples structured thermal model

^

qh

Tv

Vicinity

S2

S1

1/C

RoomThermal

Capacitance

Gs

Radiation

Tw

Outdoor

1s

Intg

55 Heater Temp.

h

Heat

aw.s+1

1

Gw

av.s+1

1

Gv

Kr

av.s+1

Gr

ah.s+1

1

Gh

ac.s+1

1

Gc

Kc

Kh

Kw

Kv

c

Cool

4 Chiller Temp.

Tc

Th

qdTiv qv

Tiw

qr

qw

qc

qu

Tih

Tic

To


Heat flow obtained with the Heat flow obtained with the firstfirst--principles structured identificationprinciples structured identification

12 14 16 18 20 22 24 26 28 30 32-6

-4

-2

0

2

4

6x 10

-3

t/hours

Heat flow parcels (assuming C=1)

qr

qv

qwqh

qc


12 14 16 18 20 22 24 26 28 30 32

18

20

22

24Results - Training data - March 29-31, 2006

12 14 16 18 20 22 24 26 28 30 32

18

20

22

24

Tc/°C - Tc est/°C - Validation - March 24-27, 2006

12 14 16 18 20 22 24 26 28 30 32

0

5

10

15

20Heat, Cool, Weather and Solar Radiation - March 24-27, 2006

Tc

Tcest

Tc

Tcest

h

-c

Ts

Gs*0.05

Results of the first-principles structured identification


PROBRALPROBRAL--RelatedRelated

SAPIEnSAPIEn––EnergyEnergySavingSavingbyby FuzzyFuzzyDistributedDistributedControlControl

�� EnergyEnergy SavingSaving

�� ThermalThermal ComfortComfort

�� BuildingBuilding AutomationAutomation

�� FAPFAP--DF DF suportsuport

Campus UnB (2007) 995 window air conditioners Campus UnB (2007) 995 window air conditioners >R$1.000.000,00/Year (1>R$1.000.000,00/Year (1€€ ≈≈ 2,56 R$)2,56 R$)


Experimental Experimental EnvironmentEnvironment


INTERFACE GRINTERFACE GRÁÁFICA FICA ActionViewActionView®®


TELA DE ALARMETELA DE ALARME


SIMULATIONSIMULATIONSimulinkSimulink®®

MIMO process:MIMO process:

�� 4 rooms4 rooms

�� 3 air conditioners3 air conditioners

ref sq1

ref sq

T_ext2

T_ext1

T_v ic1

T_v ic2

T_v ic3

T4

Room 4

extern disturb

T_v ic2

T_v ic4

Cool_on_of f

Tc3

T3

Room 3

extern disturb

T_v ic1

T_v ic3

T_v ic4

Cool_on_of f

Tc2

T2

Room 2

extern disturb

T_v ic4

T_v ic2

Cool_on_of f

Tc1

T1

Room 1

Rad_S2

Rad_S1

OPOper.

Point1

OPOper.Point

-1

62.18

577.4

KWh / ISE

ref

dif Ext

y

PWM

KWh/ISE

Fuzzy-I AWUpCtrl3

ref

dif Ext

y

PWM

KWh/ISE

Fuzzy-I AWUpCtrl2

ref

dif Ext

y

PWM

KWh/ISE

Fuzzy-I AWUpCtrl1

Fuzzy

ref


Room ModelRoom Model

2

T2

1

Tc2

S2

1/C2

RoomThermal

Capacitance

1s

Intg

Tw Tiw

Gw2

Tf Tiv

Gv3

Tf Tiv

Gv2

Tf Tiv

Gv1

Gss qr

Gr2

Gss qr

Gc2

Kc

Kw

Kv

Kv

Kv

4

Chil ler Temp.

5

Cool_on_off

4

T_vic4

3

T_vic3

2

T_vic1

1

externdisturb

Tc

qd

Tiv

Tiw

qr

qcTic

T2

Tiv

qv

qw


ControllerController

2

KWh/ISE

1

PWM

==~

predialSpin

Switch

S2

Prod.

InOut

Pass if Work Hour1

InOut

Pass if Work Hour PWM1

-K-

Ki11s

Integ.

1s

I0

3

y

2

difExt

1

ref

e


Fuzzy Rule BasisFuzzy Rule Basis

1) IF (erro = p) AND (setpoint = set) AND (Difext = d ) THEN (output = p)

2) IF (erro = n) THEN (output = zero)


SimulationSimulationResultsResults

500 600 700 800 900 1000 1100 1200 130020

22

24

26

28

°C

Room Temperatures - Air conditioner's internal on-off control

500 600 700 800 900 1000 1100 1200 130023

24

25

26

27

28

°C

Room Temperatures - MIMO Fuzzy-I Control

500 600 700 800 900 1000 1100 1200 13000

10

20

30

°C

PWM heat flow signal - MIMO Fuzzy-I Control

T1

T2

T3T4

"ref"

T1

T2

T3T4

ref

Tc1

Tc2Tc3


DistributedDistributed buildingbuilding controllercontroller PrototypePrototypeProgrammable Logic Controller

KMC 7000 (~R$300,-)


Developing Room

On-Off 16-09-2006

Fuzzy Control 14-09-2006

22,0024,00

26,0028,00

30,0032,00

34,0036,00

38,0008

:00

08:3

5

09:1

0

09:4

5

10:2

0

11:0

0

12:0

5

12:4

0

13:1

5

13:5

0

14:2

5

15:0

0

15:3

5

16:1

0

16:4

5

17:2

0

17:5

5

ºC

Setpoint

T. Interna

T. Externa

Inf

Sup

22,00

24,0026,00

28,00

30,0032,00

34,00

36,0038,00

40,00

08:0

0

08:3

5

09:1

0

09:4

5

10:2

0

11:0

0

12:0

5

12:4

0

13:1

5

13:5

0

14:2

5

15:0

0

15:3

5

16:1

0

16:4

5

17:2

0

17:5

5

ºC

S etpoint

T. Interna

T. Ex terna

Inf

S up~30% saving!


Measured and Reconstructed Signals Measured and Reconstructed Signals -- MultiMulti--Room Experiment (June 2007)Room Experiment (June 2007)


Intelligent Building Automation Intelligent Building Automation Wire Less Prototype Wire Less Prototype –– LAVSILAVSI--LARA/ENELARA/ENE--UnBUnB

Singular Singular SystemsSystems((DescriptorDescriptor SystemsSystems))

João João YoshiyukiYoshiyuki IshiharaIshihara


OutlineOutline

�� IntroductionIntroduction

�� WhatWhat is a singular is a singular systemsystem((descriptordescriptor systemsystem)?)?

�� ExamplesExamples of of descriptordescriptor modelingmodeling

�� Some Some resultsresults: : stabilitystability, , filteringfiltering

�� FutureFuture workwork relatedrelated to PROBRALto PROBRAL


WhatWhat is a singular is a singular systemsystem ((descriptordescriptor)?)?

( ) ( ) ( )( ) 0,, =tutxtxf &

( ) ( ) ( )tButAxtxE +=&Linear case

In particular:

( ) ( ) ( )tButAxtx +=&

Implicit equations

Singular equations

Usual state-space equations


WhatWhat is a singular is a singular systemsystem ((descriptordescriptor)?)?

( ) ( ) ( )tButAxtxE +=&

Another characterization

Can be re-written as:

( ) ( ) ( )( ) ( )tJutHx

tGutFxtx

+=+=

0

&

Differential-algebraic equations


DescriptorDescriptor modelmodel

( )( )( )( )

( )( )( )( )

( )

( ) [ ] ( )txty

tv

tv

tv

tv

tI

R

C

tv

tv

tv

tIL

S

R

C

L

R

C

L

0100

1

0

0

0

1110

100

000/1

0010

0000

0000

0100

000

=

+

−=

&

&

&

&

( ) ( )( ) ( )

( ) ( )( ) ( ) ( ) ( )tvtvtvtv

tvtRI

tICdt

tdv

tvdt

tdIL

SRCL

R

C

L

+++=+−=

=

=

0

0

1

RLC circuit equations

descriptor equations

( ) ( ) ( )tButAxtxE +=&


ExampleExample: : RobotRobot withwith RestrictedRestricted MotionMotion

Minimização de energia para robôs com restrição Trabalho de mestrado no LAC - Aluno: Cauê PeresOrientador: Paulo Sérgio P. da SilvaAno - 2003


RobotRobot withwith RestrictedRestricted MotionMotion

uSx

F

FDK

I

xM

IT

+

−−=

0

0

00

00

000

00

00

0

0

000

3

0

3

&


Example: Perturbation analysis of large systems

( ) ( ) ( )kBukAxkEx +=+1

( ) ( ) ( ) ( )( ) ( ) ( ) ( )kuBkxAkxAkx

kuBkxAkxAkx

22221212

12121111

1

1

++=+++=+

ε

( ) ( ) ( ) ( )( ) ( ) ( )kuBkxAkxA

kuBkxAkxAkx

2222121

12121111

0

1

++=++=+

Large systems: small parameters are set to zero

Simplified model


Example: interconnected systems

( ) ( ) ( )( ) ( ) ( )kJukHky

kGukFkE

+=+=+

ξξξ

1

( ) ( ) ( )( ) ( ) ( )kwDkxCkz

kwBkxAkx

iiiii

iiiii

+=+=+1

( ) ( ) ( ) ( )( ) ( ) ( ) ( )kRwkQukPzky

kNykMukKzkw

++=++=

Large systems: collection of interconnected subsystems

Overall subsystem equation

( ) ( ) ( )( ) ( ) ( )kDwkCxkz

kBwkAxkx

+=+=+1

Interconnections: overall input u(k) and overall output y(k)

Obs: A state-space representation may not exist for some interconnected systems


A system with no state equations

33 RRR iiv +−=

[ ]xy

u

x

xx

C

010

0

1

0

0

0

301

011

100

000

000

00

3

=

=

+

−+

&


Example 2: system with no state equations

Agricultural production growing curve

Logistic function

3 parts:* Exponential growth* Linear growth* Logaritmic growth


Example 2: system with no state equations

The agricultural production growing curve is solution of the following singular system


Transistor model

( ) ( ) ( )

[ ]xRy

i

vxttutxtx

C

E

C

0

,0 ,1

0

01

10

00

0

=

=≥

+

=

&

Impulsive Responses

In the state space formulation we have information loss(Impulsive solution)


Example: Filtering of non causal system

( ) ( ) ( )( ) ( )kHxkz

kGukFxkEx

=+=+1

( ) ( ) ( ) ( )[ ]TTTT pkzkzpkzkx +−=

=

001000

000100H

T

G

=

100000

010000

+−

+

+−

+

=

=

22

22

1010

10

01

0101

100000

010000

001000

000100

,

000000

000000

001000

000100

000010

000001

FE


Pros Pros andand Cons for Cons for descriptordescriptor modelingmodeling

�� RiccatiRiccati equationsequations are are relatedrelated to to HamiltonianHamiltonianextendedextended pencilspencils((whichwhich are in are in descriptordescriptor formform))

�� StateState--space systems are not closed space systems are not closed under PID feedbackunder PID feedback

�� Any rational transfer function can be Any rational transfer function can be represented by a descriptor systemrepresented by a descriptor system


Pros Pros andand Cons for Cons for descriptordescriptor modelingmodeling

�� It is a It is a veryvery natural natural wayway to to modelmodel processprocessdynamicsdynamics..

�� It It refersrefers muchmuch more to more to thethe physicalphysical behaviorbehaviorof of thethe systemsystem andand givesgives more more physicalphysicalinsight.insight.

�� TheThe interpretationinterpretation of of resultsresults is is alsoalso more more simplesimple thanthan in case of in case of thethe more abstract more abstract statestate spacespace modelsmodels..


AplicationAplication: : LiquidLiquid LevelLevel ControlControl SystemSystem


q

A

Ah

h

RRA

RR

RA

RARA

h

h

+

+−

−=

2

1

2

1

212

21

12

1111

2

1

4

14

3

1

11

&

&

q

q

q

h

h

R-

R-

-

-

q

q

h

h

A

A

+

=

0

0

41

43

010

011

1100

0100

0000

0000

000

000

2

1

2

1

2

1

2

1

2

1

2

1

&

&

&

&

State Space Model

Descriptor Model

AplicationAplication: : LiquidLiquid LevelLevel ControlControl SystemSystem


q

q

h

h

R-

R-

-KK

-KK

q

q

h

h

A

A

−−−−

=

2

1

2

1

2

1

24

114

124

314

3

2

1

2

1

2

1

010

011

11

01

0000

0000

000

000

&

&

&

&

s xpp

pp

h

h

RRA

KRR

RA

KRA

KR

RA

KR

h

h

≡

++−−

−+−=

2221

1211

2

1

212

221

12

1

11

21

11

11

2

1

4

44

4

44

34

4

34

&

&

State feedback[ ]

−=−=

2

121 h

hKKxKq ss

[ ][ ]Tdd qqhhKKxKq 212121 00−=−=

LiquidLiquid LevelLevel ControlControl SystemSystem -- ClosedClosed looploop


DescriptorDescriptor versionsversions

0,0 ≥=++ PEECCPAEPEA TTTT

Example Stability: Classical Result in the Literature

The extension of state space resultsto descriptor systems are not direct.

0=++ CCPAPA TT

0,0 ≥=++ PCECEPAEPEA TTTT

0,0 ≥==++ EXXECCXAXA TTTT

0,0)()( 00 ≥=++++ PCCAQEPEQEPEA TTT

( ) ( ) ( )( ) ( ) ( )tButAxty

tButAxtx

+=+=

&

( ) ( ) ( )( ) ( ) ( )tButAxty

tButAxtxE

+=+=

&

(Ishihara et al., 2002)


Kalman type Filter for Singular Systems

11111

11

+++++

++

+=+=

iiiii

iiiiii

vKxHz

wGxFxE

The filtered estimates recursion

[ ][ ]

[ ]

−

= +

−

++

+

+

+

+

+

++++

I

z

x

HE

K

G

I

I

FI

H

E

K

GF

I

I

RS

SQ

IP

I

Px i

ii

Ti

Ti

Ti

Ti

Ti

i

i

i

ii

iTi

ii

iiT

iiii

0

00

0

0ˆ

0

0

0

0

0

0

0

0

ˆ 1

|

1

11

1

1

1

1

1

|

1|11|1

=

++ 11

covi

Ti

ii

i

i

RS

SQ

v

w

(Ishihara et al., 2005)


Kalman type filter for Singular systems

( ) 11

1111

1

|11|1 ][ −+

−+++

−+++ ++= ii

Tii

Tiiiii

Tiii HRHEFPFQEP

The filtered estimates recursion

( ) 11111|1|

1|11|11|1 ˆˆ +

−++++

−+++++ ++= ii

Tiiiiii

Tiiiii

Tiiiii zRHPxFFPFQEPx

[ ][ ]

[ ]

−

= +

++

+

+

+

+

+

++++

I

z

x

HE

K

G

I

I

FI

H

E

K

GF

I

I

RS

SQ

IP

I

Px i

ii

Ti

Ti

Ti

Ti

Ti

i

i

i

ii

iTi

ii

iiT

iiii

0

00

0

0ˆ

0

0

0

0

0

0

0

0

ˆ 1

|

11

1

1

1

1

1

|

1|11|1

Can be rewritten as

IKIGS iii === ,,0 If

TheThe a priori a priori filterfilter is is notnot equivalentequivalent to to thethe a posteriori a posteriori filterfilter


RobustRobust FilteringFiltering

�� SystemSystem withwith uncertaintiesuncertainties ::

iiiii

iiiiiii

vxHHz

wxFFxEE

++=++=+ +++

)(

)()( 111

0000

>

=

Ε

iji

iji

T

i

i

i

i

R

Q

P

v

w

x

v

w

x

δδ

1

,,

1,,1

,,

≤∆

∆=

∆=

∆=

++

i

ihiihi

ieiifi

ifiifi

NMH

NME

NMF

kkkkkk xxx |1|1| ˆ,ˆ ,ˆ −+⇒


Kalman-type Robust Filter

Robust filter

iiiii

iiiiiii

vxHHz

wxFFxEE

++=++=+ +++

)(

)()( 111

( ) iiifTieiiiif

Tieii

Tiiiii

Tiiiii xNNPINNFFPFQEPx |,1,|,1,

1|11|11|1 ˆ)}ˆˆ](ˆˆˆˆ[{ˆ ++

−+++++ −++=

11111|1

ˆ+

−+++++ ii

Tiii zRHP

( )])ˆ([ˆ

ˆˆˆˆˆ

1,1

,|,1,1,1,

11111

1|1

11|1

+−

+++

+−+++

−+

−++

+++

++=

ieT

ifiiifiT

ieihT

ihi

iiTii

Tiiiii

Tiii

NNPNINNN

HRHEFPFQEP


Future work related to PROBRALFuture work related to PROBRAL1) 1) DescriptorDescriptor systemssystems withwith MarkovianMarkovian jumpingjumping

( ) ( ) ( )

( ) ( ) iiiii

iiiiii

vKxHz

wGxFxE

θθ

θθθ

+=

+=++ 11

AlreadyAlready donedone: : arrayarray algorithmsalgorithms for for filtersfilters of of MarkovianMarkovian jumpingjumping Linear Linear systemssystems

2) 2) otherother possibilitiespossibilities??


Conclusion & PerspectivesConclusion & Perspectives

�� Wire Less Automated Building Wire Less Automated Building as NCS Test Bedas NCS Test Bed

�� Modelling of Thermal ProcessesModelling of Thermal Processes�� Stability Analysis of NCS + Distributed AutomationStability Analysis of NCS + Distributed Automation�� SpecificSpecificHardware x COTSHardware x COTS

for for distributeddistributedautomationautomation

? UnB + KL ?? UnB + KL ?


DankeDanke! Obrigado!! Obrigado!

WirWir freuenfreuenuns uns aufauf diedie ZusammenarbeitZusammenarbeit!!

[email protected]@unb.brwww.www.lavsilavsi..eneene.unb..unb.brbr

www.www.eneene.unb..unb.br/adolfobr/adolfo

probral kl july2007 - engenharia elétrica · projects: sapien , ct -energ and finep -inova energy...

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