network control systems using scheduling strategies dr. héctor benítez pérez iimas unam
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Network Control Systems using Scheduling Strategies
Dr. Héctor Benítez Pérez
IIMAS UNAM
Objectives of NCS and Reconfigurable Control
To modify the control law based upon external factors such as Time Delays
Take into account time delays based upon the distributed system communication.
Being capable to keep an efficient response even though there is a fault and local time delays.
Objectives of NCS and Reconfigurable Control
To study dynamic schedulling in Real-Time considering how to manage processes, their communication and the related reconfiguration.
To study the dynamic effects of the computer network onto the control law.
Areas of Study
To Model Real-Time Systems
To model stochastic behaviour using TKS
To study the iteraction amongst dynamic systems and complex computer systems.
Classic Configuration
"Smart" Sensor
"Smart"Actuator
ControllerPlant"Smart" Sensor
Fault ToleranceModule
External Fault Tolerance
Module
Classic approximation based upon Queues
Messages Queue
Sensors Plant
Messages Queue
Controller
Actuators
Time Delays associated to perturbated external
processes.
Codesign Strategy
SCHEDULLING EVALUATION
StabilityTest
Time DelaysEvaluation
Valid Scheduler
Reconfiguration Proposal
Yes
NoYesNo
Scheduler Proposal
External Event
In here Reconfiguration
takes place
What is the studied iteraction
Reconfiguration
Request Plan
Validation Plan
Valid Plan
Database
Bus Controller
Node
Control Law
Node
Selection of the
Related Control
Law
Database
Control Laws
Computer Network
(Sensor Network)
Yes
(If the Plan
is valid
The related Control Law
is chosen)
No
(Rejection of the
proposed Plan)
First Reconfiguration Stage
Second Reconfiguration Stage
External Factor to requestreconfiguration
Time Delays Managment
ts
tc
ta
Sender/ Sensor
Receiver/Actuator
Controller
Time
Time
Time
Time Spent by QueuingInter-Communication
tqa
tqs
tc
T jT j+1
Lost Queue
Partial adds of transmission-times
ts
tc
ta
Sender/ Sensor
Receiver/Actuator
Controller
Time
Time
Time
Time Spent by QueuingInter-Communication
tqa
tqs
tqc
qaqccca
qsssc
tttt
ttt
Time Delays Management considering local faults
Time
Time
Time
Time
TimeControlAlgorithm
ControlAlgorithm
DecisionMaker
Sensor I
Sensor III
Sensor II
Time Managment considering different scenarios
Sensor 1
Sensor 3
Sensor 2
Not expected Process
Not expected Process
Fault Module
Actuator
Control
Actuator
Agente 3
Agente 2
Agente 1
…Considering several communication stages
Communiaction Network
Ope
ratin
g S
yste
ms
Sof
twar
e A
pplic
atio
n
Deadline Deadline Deadline
Sensor 1
Actuator
Controller
Sensor 3
Sensor 2
ActuadorActuator
Involved Processes onto
the Event
Partial Time Adding as the definition of particular scenarios
sensors
controllers
actuators
Total time Consumed by system
TimeT
jtt
Where the delays come from?From Process of Concurrency managment
11
N
i i
ii
Pcc
U
Where Ci is the processor consumed time
ic It is the uncertainty associated to the consumed time
Where the delays come from?From Process of Concurrency managment
Schedulling distributed processes using Neural Networks such as ART2A.
Processes schedulling based upon the worst case scenario under dynamic conditions.
Process managment optimization considering the communication period modification
It is of particular interes to manages the computer network system through
Communication Frecuencies
Fuzzy Approximation to the plant
kuBkxA1kxthenisAandxisAkifx:Rjjj22j11j
kxAw
kwkv
iijji
n
ijij
1
m
ii
m
jjjj
kv
kuBkxAkv
kx
1
11
The Related approximation to the state space representation
The discret plant considering time delays:
BdTAB
ikuBkAxkx
ki
ki
t
t
ki
l
i
ki
1
)exp(
10
where l=1 due to maximum time delay is one.
The related approximation amongst time delays and faults
N
1i
M
1j
τ
τ
τta
ii
p i
1ji
j
p
dτeBB
)()( 1iii
Tii pwQSRSQSu
Control design following a predictive approach
The recursive horizont development
,
SSS
SSS
SS=S
cN2NpN2Np
N1NpN
N1Np
p
Pp
p
1
11
1
2
12
10
dj nj=s 0y d
Na
=i
Nb
=i
piijij n>jB+Sa=s
1 1
Na
=j
Nb
=jd
pjjjiji c+i+njkuBb+pa=p
1 1
Time Delays Diagram
l
k Na Nbnd
time
Sampling Period k
Horizonts Na y Nb
As in terms of Feedback Control Loop
N
=ii
N
=iikikik
Tiki
)u,(yΩ
)p(wQSR+SQS)u,(yΩ=ku
1'
1,,
1
,,'
N
=ii
N
=iN
=ii
N
=iikikik
Tiki
pii
)u,(yD
)u,(yΩ
)p(wQSR+SQS)u,(yΩB+kxa)u,(yD
=+kx
i
1'
1
1'
1,,
1
,,'
'
1
Following an Optimization Procedure to tune the related Control Law
AB N
=kkk
N
=kk
pk uδ+)Cx(krefB=J
1
2
1
21
2
1
1'
1
1
'
1
2
1'
1' 22
Ap i N
=kN
=ii
N
=ikkk
Tki
k
N
=kN
=ii
N
=i
pii
kpk
)u,(yΩ
)p(wQSR+SQS)u,(yΩδ+
)u,(yD
kuB+kxa)u,(yDCrefB=J
The related Numeric Optimization
BN
=kk
pkp
k
)Cx(krefB=J
1
12B
AN
=kkk
k
uδ=δJ
1
2
N
=ii
N
=iiN
=kN
=ii
N
=i
pii
kpk
i )u,(yD
)x(k)u,(yD
)u,(yD
kuB+kxa)u,(yDCrefBC=
aJ p i
1'
1'
1
1'
1' 222
2
….The related Numeric optimization
N
=ii
N
=kN
=ii
N
=i
pii
kp
p
)u,(yD
)Cu(k
)u,(yD
kuB+kxa)u,(yDCrefB=
B
J p i
k
i
1'
1
1'
1' 2
222
N
=ii
N
=i
piN
=kk
p
i )u,(yD
kNxkuB+kxakCxrefBC=
D
Jip
k
1'
1
1
)1(22)1(2
AN
=kN
=ii
N
=ikkk
Tk
ki )u,(yΩ
kNu)p(wQSR+SQSkuδ=
J
1
1'
1
1)(
)(2
Where the related optimized parameters are…
pN
=j
N
k
i
σ
cu
σ
cy
σ
cy
σ
cy=
c
D p
kj 1,
2
uij
uij'
2
yij
yij
2
yik
yik
12y
ik
yik
yij
expexp2
pN
=j
N
k
i
σ
cu
σ
cy
σ
cu
σ
cu=
c
D p
kj 1,
2
uij
uij'
2
yij
yij
2
uik
uik'
12u
ik
uik'
uij
expexp2
Cases of Study
AIRPLANE
THREE BANDS
MAGNETIC LEVITATOR
HELICOPTER
System Simmulation considering aerodynamic modelling
Data Data Data
Satellite dish
Three Bands Case Study
MC
MC
MC
MCMC
MCMC
MC
MCMC
MCMC
MCMC
MCMC
Controller Bus Controller
Conveyor belt 1
Conveyor belt 2
Conveyor belt 3
s11
s12 s1
3
s110
s21s2
2 s23
s210
s310
s31 s3
2s3
3
Magnetic Levitation Case Study
Magnetic Levitation Case Study
Magnetic Levitation Case Study
Processes management in closed distributed systems enviroment
Agent Integration
The use of schedulling to define process behaviour
Preliminar Results
Modifying conditions on the scheduling algorithmRelated to based period and the increment of possible uncertainties
Preliminar Results from the control Point of View
Multi-Variable Case StudyHelicopter
Preliminar Results from the control Point of View
Preliminar Results
The Designed Algorithms
Different models based upon schedulling algorithm following an optimization procedure.
Designing a control strategy following bounded time delays.
Conclusions
The Reconfiguration as a strategy to keep certain efficiency even in the case of a fault scenario.
To understand time delays as result of reconfiguration procedure.
Acknowledgments
Dr. Jorge Ortega Arjona Miguel Palomera Pérez Oscar Alejandro Esquivel Paul Erick Mendez Monroy Dr. Antonio Menendez Leonel de Cervantes Dr. Pedro Quiñones Reyes Magali Arellano Angel Garcìa Zavala William Sanchez Dr. Eduardo Pérez
The use of schedulling to define process behaviour