indin 2012 beijing zongxia jiao yousef ibrahim … talks 2017/dec 2012 indin...input 1 u1 event-ugs1...

INDIN 2012 BeijingZongxia Jiao Yousef Ibrahim EchoShaoping WangLiang YanFei Tao

ZhangJingbing

UTA Research Institute (UTARI)The University of Texas at Arlington

F.L. Lewis, Fellow IEEE, Fellow IFACMoncrief-O’Donnell Endowed ChairHead, Controls and Sensors Group

http://ARRI.uta.edu/acs

Discrete Event Control for Dynamic Resource Allocation in Networked Manufacturing Systems

He who exerts his mind to the utmost knows nature’s pattern.The way of learning is none other than finding patterns of the lost mind.

Meng Tze

Springer-Verlag 2006

Networked Supervisory Control (NSC)

Dynamic Resource Optimisation

Total Operations Visibility

Adaptive Real Time Factory Automation & Energy Management

Control Architecture; System Modelling and Analysis

ConstraintsQoS Mgt

Specifications

Interpretation

EnforcementDecisionExecuter

Monitored Events & Dynamics

ConfigurationContext Conditioning

Generic Rules

Local ControllerSensor/RFID Networks Wireless Devices

Optimisation Engine

Performance AnalysisEnergy

Labour

Material

Machine

Order

Quality

2

54

3

183%

Loading

Intelligent Fault Recovery

Real Time Task Sequencing

Sensors

MANAGING INDUSTRIAL INFORMATICS

Discrete Event Systems Task Sequencing Resource Assignment

Discrete Event Supervisory Control

Objective:Develop new DE control algorithms for decision-

making, supervision, & resource assignment

Apply to manufacturing workcell control, C&C systems, & i-networked systems

Results:• Patent on Discrete Event Supervisory Controller • New DE Control Algorithms based on Matrices• Implemented on Intelligent Robotic Workcell• Implemented on Wireless Sensor Networks• Internet- Remote Site Control and Monitoring

USA/Mexico Internetworked Control

Man/Machine User Interface

TexasTexas

Intelligent Robot Workcell

Fast programming of multiple missions Real-time event response Dynamic assignment of shared resources

UTARI Intelligent Material Handling (IMH) Cell3 robots, 3 conveyors, two part paths

EXAMPLE

Layout of the IMH Cell

X5

X2X8

X4

X6

X7

X3

X9X1

R1

R3 R2

M2 M1

B3

B2

B1 A B A B

IBM robot

PUMA robotADEPT robot

Conveyorbidirectional Conveyor

unidirectional

conveyor

machinemachine

Reentrant Flow Line PART B OUT PART A OUT PART A PART B

CRS

ROBOT 1

ROBOT 2

ROBOT 3

Machine 1

Machine 2

A(1)R1

A(2)R1 B(1)R1

B(2)R1

A(1)R2

A(2)R2

B(1)R2

B(1)R3

B(2)R3 A(1)R3

PUMA

ADEPT

Routing and Dispatching Decisions are Needed

Wireless Sensor NetworksUTARI Distributed Intelligence & Autonomy Lab- DIAL

UnattendedGroundSensors

SmallmobileSensor-Dan Popa

Testbed containing MICA2 network (circle), Cricket network (triangle), Sentry robots, Garcia Robots & ARRI-bots

Dan Popa

Programmable MissionsMission Programming and Execution

Mission Programming for Distributed Networks

R1

R2

R3

UGS1

UGS2

UGS3

UGS4

UGS5

Deploy & Program

Vincenzo Giordano

Mission1- Alarm DetectionTask sequence

mission1 notation Description

Input 1 u1 EVENT- UGS1 detects chemical alert

Task 1 S4m1 UGS4 takes measurement


Task 3 R1gS21 R1 goes to UGS2

Task 4 R2gA1 R2 goes to location A

Task 5 R1rS21 R1 retrieves UGS2

Task 6 R1lis1 R1 listens for interrupts

Task 7 R1gS11 R1 gores to UGS1

Task 8 R2m1 R2 takes measurement

Task 9 R1dS21 R1 deploys UGS2


Task 11 S2m1 S2 takes measurement

output y1 Mission 1 completed

Mission 2- Low BatteryTask sequence

Mission2 notation Description

input u2 EVENT- UGS3 batteries are low


Task 2 R1g S32 R1 goes to UGS3

Task 3 R1cS32 R1 charges UGS3


Task 5 R1dC2 R1 docks the charger


Fast Programming of Missions

WSN is also a Reentrant Flow Line!

PART B OUT PART A OUT PART A PART B

CRS

ROBOT 1

ROBOT 2

ROBOT 3

Machine 1

Machine 2

A(1)R1

A(2)R1 B(1)R1

B(2)R1

A(1)R2

A(2)R2

B(1)R2

B(1)R3

B(2)R3 A(1)R3

PUMA

ADEPT

Target/Event/ UGS/

UGS/

UGS/

Target/Event/

Routing and Dispatching Decisions are Needed

Sample Team Mission scenario from J. Albus talk, ASC Orlando, Dec. 2008.

A sequence of tasks triggered by detected events = A Mission

Task1 TT1 M1 R1 M2 R2 M4 …

Task2 TT2 M2 R2 M3 TT1 M2 …

Task3 TT1 M3 R1 M1 TT2 M1 …

Task flow information in Industrial Processing

T1 xport mill insert grind move smooth

T2 xport grind move High polish

xport grind

T3 xport polish insert mill xport mill

TT1 TT2 R1 R2 M1 M2 M3 M4xport 1 1mill 1grind 1Polish/smooth 1 1move 1insert 1High polish 1

1. Job sequencing

2. Resource Assignment

Task Sequencing Matrix - TSM

Task 1Task 2Task 3Task 4Task 5Task 6


Task

1Ta

sk 2

Task

3Ta

sk 4

Task

5Ta

sk 6

Nextjobs

Prerequisitejobs

Used by Steward in ManufacturingTask Sequencing

Mission 1tasks

Mission 2tasks Partial order

time sequencing

Mission 1

Mission 2

Task

1Ta

sk 2

Task

3Ta

sk 4

Task

5Ta

sk 6

Mission 1tasks

Mission 2tasks

Contains same informationas the Bill of Materials(BOM)

Resource Assignment Matrix RAMUsed by Kusiak in ManufacturingResource Assignment

Contains informationabout required resources

Nextjobs

Prerequisiteresources

Task 1Task 2Task 3

Task 1Task 2Task 3

UG

S 1

UG

S 3

robo

t 1

UG

V 1

UG

V 2

UAV

1

Mission 1tasks

Mission 2tasks

RAM can change in real time as resources fail or are added


Discrete Event Controller

Workcell / WSNJobsdone

Resourcesavailable

Start jobs

Assign resources se

nsor

s

OutputsCommands

Trigger Eventsparts intargets detected

Discrete EventSupervisoryController

Computer software

FEEDBACK CONTROL

Darwin – Natural Selection of SpeciesVolterra – Population BalanceAdam Smith - EconomicsJames Watt – Steam Engine

SensoroutputsTaskscompleteResourcesavailable

Missioncomplete

StarttasksReleaseresources

Trigger events

TeamStatus Commands

Real-Time Decision Interrupts and Mission priority

Distributed Team

Messagepassing

Real-timeOperationalPhase

PlanningPhase

Force Commander Goal PrioritiesHigh-level Planning

Mission CommanderMission plan & goalsRequired operations

On-Site CommanderResources availableAssign manpower & platformsMaterial requirements planning

Chain ofCommand

PC, Laptop, PDA

PC, Laptop, PDA

PC, Laptop, PDA

C&C Rule-Based Discrete Event Controller for Distributed Networked Teams

What goes here?

Messagepassing

Rule-Based Controller = Discrete event system

IF Part A entersAND

Fixture 1 is availableAND

Robot 1 is available

THEN Robot 1 pick up part A AND

put into fixture 1

TWO TYPES of information-

Tasks just completed

Resources available

Task sequencing

Resource assignment

IF (tasks just completed) AND (resources available)THEN (perform next task)

tasks prerequisite resources neededt1 t2 t3 t4 r1 r2 r3

Rule 1 1 1 1Rule 2 1 1Rule 3 1 1Rule 4 1 1 1 1

rule firedR1 R2 R3 R4

Task 1 start 1Task 2 start 1Task 3 start 1Task 4 start 1

Rule i:IF (the tasks required as immediate precursors to task i are complete)

AND (the resources required for task i are available) THEN fire rule i

Introduce Rule Base

IF (rule i fires) THEN start task i

How to find the rules? Expert system?

Problems with:Redundant rulesInconsistent rules

How to implement the controller

Problems with:Real-time task sequencingDynamic resource assignmentBlocking phenomena

Discrete event controller

T asksco m p le ted v c

R u le-b ased rea l tim e con tro lle r

Cu curv uFuFrFvFx

Job s ta rt lo g ic

R esource re lease lo g ic

W ireless Sensor

N etw o rk

. . .

u c

Se nsor ou tp ut u

R esourcere leased rc

S tart tasks v s

S tart reso urcere lease rs

O utp ut yM iss io n co m p le ted

P la nt co m m a nds P la nt s ta tus

D isp atch in g ru le s

C o ntro ller state m o nito ring lo g ic

xSv VS

xSr rS

xSy y T ask co m p le te lo g ic

User interface:mission planning,

resource allocation, priority rules

U.S. Patent

SensorStatus readingsevents

commands

Decision-making

Workcell / WSN

1001

cv

Job vector -Means jobs 1 and 4 have just completed

0110

cr

Resource vector -Means resources2 and 3 are available

Jobsdone

Resourcesavailable

Start jobs

Assign resources

sensors

Output status vectorscommands

Eventsparts intargets detected

DE Model State Equation:

DDucrcv uFuFrFvFx

The Secret: multiply = AND & addition = OR

Tasks complete

Resources available

Trigger Events / parts in

Decision/Command input

Task sequencing matrix – by Mission Planner

Resource assignment matrix – by onsite Leader

Rule vectorFire next tasks

New Matrix Formulation for Supervisory Control

Compare with xk+1=Axk+Buk

US Patent

1001

cv

Means jobs 1 and 4 have just completed

0110

cr

Means resources2 and 3 are available

Job Start Equation:Resource Release Equation:Product Output Equation:

xSv vs xSr rs xSy y

Compare with yk=Cxk

Output equations

Binary mathematics- LOGIC not real numbers

Meaning of Matrices

Resources requiredPrerequisite jobs

Nextjob

NextjobFv

Fr

Conditions fulfilled

Nextjob Sv

Task Sequencing Matrix Resource Requirements Matrix

Releaseresource Sr

Conditions fulfilled

Resource Release MatrixTask Start Matrix

Steward Kusiak

Example

ARRI

100001000000001000010000

vF

000010000100000000100001

TvS

000010000001

uF

1 0 00 1 10 0 10 0 11 1 00 1 0

rF

010100000010001000

TrS

100000010000

TyS

p1 p2 p3 p4 r1 r2 r3

p1 p2 p3 p4 r1 r2 r3

pinA pinB

poutA poutB

DDucrcv uFuFrFvFx xSv vs xSr rs

xSy y

First- Sequence the Mission or Job Tasks

Task sequencing Matrix- TSM

Production Planner / Mission Commandershould specify intent, not details

A general class of missions consisting of sequenced tasksEach task has a well-defined entry state and exit state – Kevin Moore

I. Planning / Programming Phase

Computer science planners (e.g. HTN) give information equivalent to TSM

Construct Task Sequencing Matrix - TSM



Task

1Ta

sk 2

Task

3Ta

sk 4

Task

5Ta

sk 6

Nextjobs

Prerequisitejobs

Used by Steward in ManufacturingTask Sequencing

Mission 1tasks

Mission 2tasks Partial order

time sequencing

Mission 1

Mission 2

Task

1Ta

sk 2

Task

3Ta

sk 4

Task

5Ta

sk 6

Mission 1tasks

Mission 2tasks

Contains same informationas the Bill of Materials(BOM)

Computer science task planners give the Fv Matrix

e.g. hierarchical HTN task planners

Assembly Tree

a

b c

d e

0 1 1 0 00 0 0 0 00 0 0 1 10 0 0 0 00 0 0 0 0

v

a b c d eab

F cde

Two 1’s in a row= assembly

Task Sequence

Reorder tasks to get lower diagonal matrix = causal

0 0 0 0 00 0 0 0 01 1 0 0 00 0 0 0 00 0 1 1 0

v

e d c b aed

F cba

Warfield

Second- Assign the Resources

Resource Assignment Matrix - RAM

Job Shop ForemanField Commander

I. Planning / Programming Phase

Construct Resource Assignment Matrix RAMUsed by Kusiak in ManufacturingResource Assignment

Contains informationabout required resources

Nextjobs

Prerequisiteresources

Task 1Task 2Task 3

Task 1Task 2Task 3

UG

S 1

UG

S 3

robo

t 1

UG

V 1

UG

V 2

UAV

1

Mission 1tasks

Mission 2tasks

RAM can change in real time as resources fail or are added

Assign the resources – shop foremanAssembly Tree

a

b c

d e

0 1 1 0 00 0 0 0 00 0 0 1 10 0 0 0 00 0 0 0 0

v

a b c d eab

F cde

Two 1’s in a row= assembly

a

bc

d e

1 2 1 2

0 0 1 00 1 0 10 0 1 00 0 1 01 0 0 1

f f R R

r

ab

F cde

Robot 1

Fixture 1

Robot 1Fixture 2

Robot 2

Robot 2

Multiple 1’s in a col. MeansShared resource

SensoroutputsTaskscompleteResourcesavailable

Missioncomplete

StarttasksReleaseresources

Trigger events

TeamStatus Commands

C2 Rule-Based Discrete Event ControllerRule State equation

Task start equationResource release equation

Real-Time Decision Interrupts and Mission priority

Distributed Team

MessagepassingMessage

passing

Real-timeOperationalPhase

PlanningPhase

vs

rs

vc

rc

u

uD

x vc , rc

vs, rs

Force Commander Goal PrioritiesHigh-level Planning

Mission CommanderMission plan & goalsRequired operations

Battalion CommanderResources availableAssign manpower & platformsMaterial requirements planning

program Fv

Chain ofCommand

PC, Laptop, PDA

PC, Laptop, PDA

PC, Laptop, PDA program Fr

C&C Rule-Based Discrete Event Controller for Distributed Networked Tea


Discrete Event Controller How does DEC Work?

DDucrcv uFuFrFvFx xSv vs xSr rs

xSy y

Discrete Event Controller

How does it work?

The secret:addition = ORmultiply = AND

.OR.

.AND.

negation

Octav Pastravanu

crcv rFvFx

OR / AND Matrix Algebra

Example

)1()0()1(]101[ cbacba

x

)0()1()0( cbax

cax

)1()0()1( cbax

)1()0()1( cbax

De Morgan’s Laws

Don’t care about b

Easy to implement OR/ AND algebra in MATLAB

C= AB

for i= 1,Ifor j= 1,J

c(i,j)=0for k= 1,K

c(i,j)= c(i,j) .OR. ( a(i,k) .AND. b(k,j) )end

endend

Boolean Matrix multiply

Rule i:IF (the tasks required as immediate precursors to task i are complete)

AND (the resources required for task i are available) THEN fire rule i

Proper Functioning of DEC

DEC Gives a Rule base that is: Consistent Nonredundant

Can add code to guarantee: No blocking phenomena

DEC performs dynamic real-time task sequencing and resource assignment

Does not need to know durations times of jobs

Interleaves the tasks of multiple missions depending on mission priority

Guaranteed Performance due to DEC Structure

Properness of the DEC Rulebase

• Formal rigorous framework• Complete DE dynamical description• Relation to known Manufacturing notions• Formal relation to other tools- Petri Nets, MAX-Plus• Easy to design, change, debug, and test• Formal deadlock analysis technique• Easy to apply any conflict resolution (dispatching) strategy• Optimization of resources• Easy to implement in any platform (MATLAB, LabVIEW, C,

C++, visual basic, or any other)

Advantages of the Matrix Formulation


Discrete Event Controller How does DEC Work? Relation to Existing ModelsMax-PlusPetri nets

Relation to Max-Plus Algebra

DDucrcv uFuFrFvFx xSV vs xSr rs xSy y

State equation

Output equations

Define diagonal timing matrices. Then max plus is

rFTSxFTSx rrrvvv '

OPERATIONS IN OR-AND ALGEBRA

OPS. IN MAX-PLUS ALGEBRA

Can also include nonlinear terms- correspond to decisions

Diagonal Timing Matrices

Relation to Petri Nets

DDucrcv uFuFrFvFx xSV vs xSr rs xSy y

State equation

Output equations

x= transition vector – fire the rules

v, r = place vectors – store the current situation (tokens)

Relation to Petri NetsResources availableJobs complete

Trans. Trans.Fv Fr

Transition

Nextjobs Sv

Transition

Releaseresource Sr

r3

100001000000001000010000

vF

000010000100000000100001

TvS

000010000001

uF

000010100000010001

rF

010100000010001000

TrS

100000010000

TyS

p1 p2 p3 p4 r1 r2 r3

p1 p2 p3 p4 r1 r2 r3

pinA pinB

poutA poutB

pinA p1t1 t2

p3t4 t5

p2 t3

p4 t6pinB

poutA

poutB

r1

r2Jobs along path

Resources off path

Mission 1

Mission 2

DDucrcv uFuFrFvFx xSV vs xSr rs , ,


Discrete Event Controller How does DEC Work? Relation to Existing Models Complete DE Description and Simulation

Complete Dynamical Description of DE Systems

xFStmxMtmtm TTT ][)()()1(

DDucrcv uFuFrFvFx Petri Net Marking transition equation

DE state equation

Activity Completion Matrix ][ yrvu FFFFF

[ ]T T T T Tu v r yS S S S S

],,,[ yT

yrT

rvT

vuT

uT FSFSFSFSFSM

PN incidence matrix

Activity Start Matrix

T T T T Tm u v r y Marking vector

Details -

xSr rs xSV vs

xSy y

Start equations

Allows easy MATLAB simulation of DE systems

DDucrcv uFuFrFvFx

)()()1( txStmtm Tpp

)()()1( txFtmtm aa

)1()1()1( tmtmtm pa

Duration time counting routine

1. Rules- fire transitions

2. Add tokens to places

3. Wait until jobs finish

4. Take tokens from places

5. Find updated vectors for DE state equation

Controller

System model

6. )( pmyrvu TTTTT

PNMarkingTransitionEq.





Dan Popa

Mission 1 - Alarm DetectionJob sequence

mission1 notation Description

Input 1 u1 UGS1 launches chemical alert



Task 3 R1gS21 R1 goes to UGS2

Task 4 R2gA1 R2 goes to location A

Task 5 R1rS21 R1 retrieves UGS2

Task 6 R1lis1 R1 listens for interrupts

Task 7 R1gS11 R1 gores to UGS1


Task 9 R1dS21 R1 deploys UGS2


Task 11 S2m1 S2 takes measurement


Mission 2- Low Battery ChargeJob sequence

Mission2 notation Description

input u2 UGS3 batteries are low


Task 2 R1g S32 R1 goes to UGS3

Task 3 R1cS32 R1 charges UGS3


Task 5 R1dC2 R1 docks the charger


Fast Programming of Multiple Missions / Tasks

100000000000100000000000100000000000110000000000010000000000010000000000011000000000001100000000000

19

18

17

16

15

14

13

12

11

1

xxxxxxxxx

Fv

000000000010000000000000000000000110000000000000000000111100000

19

18

17

16

15

14

13

12

11

1

xxxxxxxxx

Fr

Mission 1 matrices

1 2 3 4 5

21222

2 3242526

0 0 0 0 01 0 0 0 00 1 0 0 00 0 1 0 00 0 0 1 00 0 0 0 1

v

t t t t t

xxx

Fxxx

21222

2 3242526

1 2 1 2 3 4 50 0 1 0 0 0 01 0 0 0 0 0 00 0 0 0 0 0 00 0 0 0 1 0 01 0 0 0 0 0 00 0 0 0 0 0 0

r

R R S S S S Sxxx

Fxxx

1 1

2 2

0,

0v r

v rv r

F FF F

F F

Mission 2 matrices

Block together matrices from multiple missions

Can all be done automatically in software

TSM RAM

Missions use the same Team resources

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140

10

20

30

40

50

60

70 1S4m1S5m1R1gS21R2gA1R1rS21R1lis1R2m1R1gS11R1dS21R1m1S2m

2S1m2R1gS32R1cS32S3m2R1dC

R1R2UGS1UGS2UGS3UGS4UGS5

Time [s]

mobile wireless sensor network DE simulation priority 2-->1

Reso

urce

s

M

issi

on2

M

issi

on 1

u2

u1

0 20 40 60 80 100 120 140 0 20 40 60 80 100 120

10

20

30

40

50

60

70 1S4m1S5m1R1gS21R2gA1R1rS21R1lis1R2m1R1gS11R1dS21R1m1S2m

2S1m2R1gS32R1cS32S3m2R1dC

R1R2UGS1UGS2UGS3UGS4UGS5

Time [s]

mobile wireless sensor network DE simulation priority1-->2

Reso

urce

s

Mis

sion

2

M

issi

on1

u1

u2

Simulation 2 –change mission/task priority

Simulation Results

Event traces

• Up means job in progress

• Down means resource in use

Mission 1 jobs

Mission 2 jobs

Resources

Resource Percent Utilization

Simulation of Steady-State Behavior

Bottleneck Detection


Discrete Event Controller How does DEC Work? Relation to Existing Models Complete DE Description and Simulation Implementation

Discrete event controllerImplementation



Cu curv uFuFrFvFx

Job s ta rt lo g ic


W ireless Sensor

N etw o rk

. . .

u c

Se nsor ou tp ut u


S tart tasks v s






xSv VS

xSr rS


User interface:mission planning,

resource allocation, priority rules

U.S. Patent

Sensor readings

events

commands

Decision-making





Dan Popa

VR interface Panoramic view of the configuration of the mobile WSN during real-world experiments

Actual Implementation of DEC

R1u1

R1u2

R1u3

R1u4

R2u1

R2u2

R2u3

R3u1

R3u2

Discrete events

Results of LabVIEW Implementation on Actual Workcell

Compare with MATLAB simulation!

We can now simulate a DE controller and then implement it,Exactly as for continuous state controllers!!

S4m

R1gS2

R1rS2

R2m

R1dS2

S2m

R1gS3

S3m

S5m

R2gA

R1lis

R1gS1

R1m

S1m

R1cS3

R1dC

Utilization time trace of the WSN- Experimental results

Remote Site Programming and DEC

Man/Machine User Interface

USA/Mexico Internetworked Control

TexasTexas

Intelligent Robot Workcell

Fast programming of multiple missions Real-time event response Task sequencing Dynamic assignment of shared resources

DEC

LabVIEW


Discrete Event Controller How does DEC Work? Relation to Existing Models Complete DE Description and Simulation Implementation Analysis of DEC and Blocking Phenomena

Shared resourcescircular waits

S4m2

S1m1

X21 X2

2 X23 X2

4 X25 X2

6

R1cS11 R1dC1

S3

R1

u1

u2

S3m2 R1gA2 R2mS22 S2m2 R2gC2

y2 Mission2 result

y1 Mission1 result

S2

X14 X1

3X12X1

1

S4

X27 X2

8

R2

R1gD2

S1

Circular Blocking - Critical subsystems cannot get full

= Initial resources available

Shared resourcescircular waits

S4m2

S1m1

X21 X2

2 X23 X2

4 X25 X2

6

R1cS11 R1dC1

S3

R1

u1

u2


y2 Mission2 result

y1 Mission1 result

S2

X14X1

3X12X1

1

S4

X27 X2

8

R2

R1gD2

S1

Critical subsystems cannot get fullShared Resources- which jobs to dispatch first?

LBFS always works – but too conservative

S4m2

S1m1

X21 X2

2 X23 X2

4 X25 X2

6

R1cS11 R1dC1

S3

R1

u1

u2


y2 Mission2 result

y1 Mission1 result

S2

X14 X1

3 X12 X1

1

S4

X27 X2

8

R2

R1gD2

S1

rrW FSG Wait Relation Graph

Easily found from matrices

Richard Wysk- Circular Waits cannot become Circular BlockingsCritical Siphons Cannot Get Full

Deadlock Analysis- easy with matrix DEC

rrW FSG (in AND/OR algebra)

i.e. if gij=1 then resource j waits for resource i

Use string algebra to find a matrix C, wherein each column represents a circular wait.

Find circular waits

C

FFCFSC vT

rT

vrTsSc =

where denotes the element-by-element matrix ‘and’ operation.

Find critical siphons

CS cannot get empty

Implement in Real Time

ARRI Intelligent Material Handling (IMH) Cell3 robots, 3 conveyors, two part paths

Need Supervisors to limit WIP in Critical Systems

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

1R1gS11S1m1R1dC

2R1gA2S3m2R2mS22S2m2R2gC2S4m2R1gA

S1R1S2R2S3S4

Time [s]

Mobile wireless sensor network DE simulationRe

sour

ces

Mis

sion

2

Mis

sion

1

Event time trace

Simulation results

Deadlock!

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

1R1gS11S1m1R1dC


S1R1S2R2S3S4

Time [s]

Mobile wireless sensor network DE simulation

Reso

urce

s

M

issi

on2

M

issi

on1

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

1R1gS11S1m1R1dC


S1R1S2R2S3S4

Time [s]

Mobile wireless sensor network DE simulation

Reso

urce

s

Mis

sion

2

Mis

sion

1

Event time trace without deadlock avoidance

Event time trace with deadlock avoidance

Simulation results




Cu curv uFuFrFvFx

Job s ta rt lo g ic


W ireless Sensor

N etw o rk

. . .

u c

Se nsor ou tp ut u


S tart tasks v s






xSv VS

xSr rS


Deadlock avoidance software here at top-

Decision-making

Sensor readings

events

commands

Decision-making


Discrete Event Controller How does DEC Work? Relation to Existing Models Complete DE Description and Simulation Implementation Analysis of DEC and Blocking PhenomenaReal-Time Structural Changes in Resources and TasksFailure Recovery




Cu curv uFuFrFvFx

Job s ta rt lo g ic


W ireless Sensor

N etw o rk

. . .

u c

Se nsor ou tp ut u


S tart tasks v s






xSv VS

xSr rS


Sensor readings

events

commands

Real-timeDecision-making

Mission Planner Modifies Matrices on the Fly

DEC= Real-time decisioncontrol loopWith guaranteed performance

S4m2

S1m1

X21 X2

2 X23 X2

4 X25 X2

6

R1cS11 R1dC1

S3

R1

u1

u2


y2 Mission2 result

y1 Mission1 result

S2

X14X1

3X12X1

1

S4

X27 X2

8

R2

R1gD2

S1

Adding Resources of Different Type

R3

Add columns to Fr

,r r

old resourcesF old F new col r

new resource

Done by mission planner

S4m2

S1m1

X21 X2

2 X23 X2

4 X25 X2

6

R1cS11 R1dC1

S3

R1

u1

u2


y2 Mission2 result

y1 Mission1 result

S2

X14 X1

3X12X1

1

S4

X27 X2

8

R2

R1gD2

S1

Adding New MissionMay need new resources

May make new SHARED resourcesAdds new pathMission Agent Coordinator

1 1

2 2

0,

0v r

v rv r

F FF F

F F

Block together matrices from multiple missions

Can all be done automatically in software

Missions use the same Team resources

Self Healing – without human intervention

New task entersMachine breakdownUnexpected delays/processing time changes

Automatically handled by DEC

Add New Task Path-Sequence jobs for new tasks - FvAssign resources for new jobs - Fr

Redistribute jobs to other resources – change Fr onlineReroute in case of failures- decision needed in task path

John Wiley, New York, 2006

S4m2

S1m1

X21 X2

2 X23 X2

4 X25 X2

6

R1cS11 R1dC1

S3

R1

u1

u2


y2 Mission2 result

y1 Mission1 result

S2

X14 X1

3X12X1

1

S4

X27 X2

8

R2

R1gD2

S1

Faults and Maintenance

Maintenance Agent Coordinator

Send to maintenance:ScheduledFault detected

Adds new pathMaintenance Jobs

Repair resource

Distributed Decision on Communication Graph Topology


Discrete Event Controller How does DEC Work? Relation to Existing Models Complete DE Description and Simulation Implementation Analysis of DEC and Blocking Phenomena Real-Time Structural Changes in Resources and Tasks

Failure Recovery Distributed Decision and DEC

over Communication Networks

Strongly connected if for all nodes i and j there is a path from i to j.

( 1) ( )i

i ij jj N

w k d w k

Standard consensus algorithm in a graph

Agents reach consensus if the graph is strongly connected

Distributed Decision on Communication Graph Topology

Distributed Resource Assignment Over Networks

DDucrcv uFuFrFvFx xSv vs xSr rs xSy y

Introduce Skill vector and skill matrix

v c c u D Dx F v s F u F u

Skills required

Nextjob

Skill Requirements Matrix

Skills required by jobs

Do not pin down the actual resources used. Only the skills needed.

Resources

Skills

Skill-Resource Matrix

r

1 1 0 0 00 1 0 0 11 0 1 0 10 0 1 1 0

r

The Ops. Res. Assignment problem- assign resources to skills

max ij ijskills j agents i

c z ij jskills i

z Cap 1ijres j

z rZ

1 0 0 0 00 1 0 0 00 0 0 0 10 0 1 0 0

Z

Assigned resource matrixOne ‘1’ in each row

v c r c u D Dx F v F r F u F u

rF Z

Can solve the assignment problem using distributed processing usingAuction algorithmsBundle-based algorithm

Resource req. matrix is

v c c u D Dx F v s F u F u

Heterogeneous Robots Consensus-based Allocation

The Heterogeneous Robots Consensus-based Allocation (HRCA) is a distributed iterative planning approach based on the Consensus-Based Bundle Algorithm (CBBA)1

Each agent iterates between 2 nested Stages: HRCA: Decentralized auction-based algorithm for heterogeneous robotsStage 1 (Consensus-based Bundle

Costruction):Phase 1: Bundle Filling(greedy selection of tasks)Phase 2: Conflict Resolution(consensus stage)

Stage 2 (Bundle Check)Phase 3: Bundle resize(overloaded bundles are partially emptied with criteria that tend to minimize the loss in terms of score

Communication with neighbors robots is required in phases 2 and 3 only.

1. Assign resources to skills a priori

2. Assign resources to skills at each event occurrence

1 1 0 0 00 1 0 0 11 0 1 0 10 0 1 1 0

r

1 0 0 0 00 1 0 0 00 0 0 0 10 0 1 0 0

Z

Overall skill-resource matrix

1 1 0 0 00 1 0 0 11 0 1 0 10 0 1 1 0

r

Skill-resource matrix for fireable jobs

Assigned resources at k-th event

TWO CHOICES

Description of the network

1 2 3 4 5

1

2

3

4

5

1 1 1 0 01 1 1 1 01 1 1 1 00 1 1 1 10 0 0 1 1

r r r r r

r

r

r

r

r

R1U1

B1AA

B1AS R2U1

M1A

M1P

B2AA B3AA

R2U3 B2AS R3U1 B3AS R1U3 PAO

B1BA B2BA M2A B3BA

PBI R1U2 B1BS R2U2 B2BS R3U2 M2P R3U3 B3BS R1U4 PBO

R1A

R2AR3A

X1 X2 X3 X4 X5 X6 X7 X8 X9

X12 X13 X14 X15 X16 X17 X18 X19X11 X20

PAI X10

Resource Interaction GraphT

r rF F Defines an undirected graph that shows which resources are involved in the same tasks with each other

R1U1

B1AA

B1AS R2U1

M1A

M1P

B2AA B3AA

R2U3 B2AS R3U1 B3AS R1U3 PAO

B1BA B2BA M2A B3BA

PBI R1U2 B1BS R2U2 B2BS R3U2 M2P R3U3 B3BS R1U4 PBO

R1A

R2AR3A

X1 X2 X3 X4 X5 X6 X7 X8 X9

X12 X13 X14 X15 X16 X17 X18 X19X11 X20

PAI X10

( )Tr r

Defines an undirected graph that shows which resources need to bid for skillsIt shows which resources may be in competition to supply skills for the tasks

( )T Tr rF F Z Z

Resource interaction graph is

A more fully connected graph is

Logical Consensus

To agree on the status of tasks done and resources available

( 1) ( )i ij jj

w k d w k Standard consensus algorithm in a graph

( 1) ( ( ))i j ij jw k d w k

Let be a semiring. Then the following consensus algorithm converges( , )

Take as logical .or.as logical .and.

To get logical consensus

1. S. Bogdan, F.L. Lewis, Z. Kovacic, and J. Mireles, Manufacturing Systems Control Design: A Matrix Based Approach, Springer-Verlag, London, 2006.

2. D. Tacconi and F.L. Lewis, “A new matrix model for discrete event systems: application to simulation,” IEEE Control Systems Magazine, pp. 62-71, Oct. 1997.

3. B. Harris, F.L. Lewis, and D.J. Cook, “Machine planning for manufacturing: dynamic resource allocation and on-line supervisory control,” J. Intelligent Manufacturing, vol. 9, pp. 413-430, 1998.

4. F.L. Lewis, A. Gurel, S. Bogdan, A. Doganalp, O. Pastravanu, “Analysis of deadlock and circular waits using a matrix model for flexible manufacturing systems,” Automatica, vol. 34, no. 9, pp. 1083-1100, 1998.

5. A. Gurel, S. Bogdan, and F.L. Lewis, "Matrix approach to deadlock-free dispatching in multi-class finite buffer flowlines," IEEE Trans. Automatic Control, vol. 45, no. 11, pp. 2086-2090, Nov. 2000.

6. B. Harris, D. Cook, F.L. Lewis, “A matrix formulation for integrating assembly trees and manufacturing resource planning (MRP) with capacity constraints,” J. Intelligent Manufacturing, vol. 13, no. 4, pp. 239-252. August 2002.

7. S. Bogdan, F.L. Lewis, Z. Kovacic, A. Gurel, and M. Stajdohar, “An implementation of the matrix-based supervisory controller of flexible manufacturing systems,” IEEE Trans. Control Systems Technol., vol. 10, no. 5, pp. 709-716, Sept. 2002.

8. V. Giordano, J.B. Zhang, D. Naso, and F.L. Lewis, “Integrated supervisory and operational control of a warehouse with a matrix-based approach,” IEEE Trans. Automation Science & Engineering, vol. 5, no. 1, pp. 53-70, Jan. 2008.

9. P. Ballal and F.L. Lewis, “Condition-based maintenance using dynamic decisions by Petri nets and Dempster Shafer theory: a matrix-based approach,” Trans. Inst. Measurement and Control, vol. 31, no. 3/4, pp. 323-340, June/Aug. 2009.

10.C.K. Pang, G. Hudas, M. Middleton, C.V. Le, O.P. Gan, and F.L. Lewis, "Discrete Event Command and Control for Networked Teams with Multiple Military Missions" J. Defense Modeling and Simulation, to appear, 2011.

11.A. Gasparri, D. Di Paola, G. Ulivi, D. Naso, F. Lewis, “Decentralized task sequencing and multiple missions control for heterogeneous robotic networks,” Proc. IEEE Int. Conf. Robotics and Automation, 2011, to appear.

12.D. Di Paola, A. Gasparri, D. Naso, and F.L. Lewis, “Decentralized Discrete-Event Modeling and Control of Task Execution for Robotic Networks,” Proc. IEEE Conf. Decision & Control, Maui, Dec. 2012, to appear.

References

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