value drivers in a changing landscape of modeling &...

2© 2016 The MathWorks, Inc.

Value Drivers in a Changing Landscape of

Modeling & Simulation

Pieter J. Mosterman

Chief Research Scientist, Director

Advanced Research & Technology Office (MARTO)

Adjunct Professor

School of Computer Science

3

$1,052

4

$11,221

Moore’s Law

5

“When one car learns something, the whole

fleet learns it”

$35,000

Moore’s Law Metcalfe’s LawElon Musk

6

A smart emergency response system

A feature classification

Evolving architectures

Future value drivers

Machines are connecting

and collaborating

Where can we have

impact, which solutions

are needed, what

challenges these

solutions, and how can we

overcome the challenges?

A smart emergency

response system

7

John R. Boyd

Michael A. Jackson





8

John R. Boyd

Michael A. Jackson





Physics

Information

Electronics

Network

9

Opportunities for

CPS ensembles

John R. Boyd

Michael A. Jackson





Physics

Information

Electronics

Network

10

Kun Zhang

University of Arizona

Enes Bilgin

Boston University

David Escobar Sanabria

University of Minnesota

2013 MathWorks Summer Research Internship

11

İzmit, Turkey, 1999

13

Where can we help make a

difference?

14

Deploy a heterogeneous fleet …

15

1002

… to serve many (changing!) requests …

16

1004

… across an uncertain infrastructure …

17

1004

… all in a time optimal manner!… all in a time optimal manner!

?

18

Simulation

Mission

Command and Control

Infrastructure

ATLAS

Biobot

KUKA

robot

Haptic

device

WiFi drone

Help request

App

Video stream

analysis

Human Machine

Interface

Google Earth

visualization

Ground

vehicle

UAV

19

2013 MathWorks Summer Research Internship: A Spectacular Challenge (Get cyber real!)

https://youtu.be/MxrySx1m8VQ?t=2m42s

20

hover transfer control control by responder

Man-machine control transfer

https://youtu.be/M3vq1ywbe10?t=41s

2013 MathWorks Summer Research Internship: A Self-flying Drone (Take cyber control!)

https://youtu.be/YytL8-zLE6E

RIVeR Lab has participated in the Global City

Teams Challenge in collaboration with Austin

Texas Fire Department, MathWorks, University

of North Texas, and Worcester Polytechnic

Institute.

22

Simulation

Mission

Command and Control

Infrastructure

ATLAS

Biobot

KUKA

robot

Haptic

device

WiFi drone

Help request

App

Video stream

analysis

Human Machine

Interface

Google Earth

visualization

Ground

vehicle

UAV

23





24

Colonel John Richard Boyd

25

The Observe-Orient-Decide-Act (OODA) loop

26

Orient

Decide

Observe Act

Environment

OODA and the stages of cognition

Perception

Interpretation

Cognition

27

Process (video)

Analyze for validity (filter, reject)

Automatic

Orient

Decide

Observe Act

Environment

Adaptive

Orient

Decide

Observe Act

Environment

Autonomous

Orient

Decide

Observe Act

Environment

Compute control signals

Map to semantic concepts

Fuse sensor data

Perception Interpretation Cognition

Reason based on knowledge

Plan for objectives and constraints

Assess alternatives

Determine course of action

Adjust control

Reconfigure behavior

Engineered systems and the stages of cognition

28

Automatic Adaptive Autonomous

Engineered systems and the stages of cognition

Process (video)

Analyze for validity (filter, reject)

Compute control signals

Map to semantic concepts

Fuse sensor data

Perception Interpretation Cognition

Reason based on knowledge

Plan for objectives and constraints

Assess alternatives

Determine course of action

Adjust control

Reconfigure behavior

29

Michael Anthony Jackson

30

specificationrequirements &

domain knowledgeprogram

Machine (mh) Environment (eh)

O

(mv)

I (ev)

Requirements engineering

A requirement is a desired relationship

among the phenomena

(e.g., actions/events, states) of the

environment

Phenomena are categorized as

– eh: controlled (or initiated) by the

environment and hidden from (i.e.,

invisible to, not shared with) the machine

– ev: controlled by the environment but

visible to (i.e., shared with) the machine

– mv: controlled by the machine but visible

to (shared with) the environment

– mh: controlled by the machine and hidden

from (i.e., not shared with) the

environment

31

A behavioral view

Program

Open loop possible behavior

Closed loop possible behavior

Property satisfying behavior

Closed loop designed behavior

Machine EnvironmentOut

In

WorldOut

In

||

32

EnvironmentMachine

Out

In

A behavioral view

Program





||

WorldOut

In

33

Machine Environment

Out

In

A behavioral view

Program





World

||

Out

In

34https://youtu.be/STlt48wXsyY?t=17s

For fully-automated container handling in large terminals

and terminal environments, Terex Port Solutions supplies

solutions with outstanding performance.

35

Individual Ensemble

Reason

Interpret

Adaptive

Autonomous

Connected

Collaborative


36

Individual Ensemble

Reason

Interpret

Adaptive

Autonomous

Connected

Collaborative


37





38

physics

H(s) G(s)

control Centrifugal governor(James Watt designed his first governor in 1788

following a suggestion from his business partner

Matthew Boulton)

39

physics

H(s) G(0)

ADC

DACECU

feature

Engine control unit(from a 1996 Chevrolet Beretta)

require

mentstests

40

App

physics

H(s) C

ADC

DACμP

G(z)

G(s)

feature

Freescale MPC561 MCU(32-bit PowerPC embedded microprocessors that

operate between 40 and 66 MHz, used in engine

controllers for General Motors)

RTOS

require

mentstests

41

App App

physics

H(s) C

ADC

DACμP

G(z)

G(s)

comms

network

comms physicsμP

G(z)

H(s)

G(s)

C

ADC

DAC

feature feature

RTOS RTOS

require

mentstests

require

mentstests

42

App App

physics

H(s)

ADC

DACμP comms

network

comms physicsμP

H(s)

ADC

DAC

G(z)

G(s)

C

feature

G(z)

G(s)

C

feature

require

mentstests

OS OS

C++

feature

view1 view2

classes

C++

feature

view1 view2

classes

require

mentstests

VM VM

43

μP μPphysics

H(s)

ADC

DACμP comms

network

comms physicsμP

H(s)

ADC

DAC

OS

VM

App

OS

VM

App

G(z)

G(s)

C

feature

G(z)

G(s)

C

feature

require

mentstests

C++

feature

view1 view2

classes

C++

feature

view1 view2

classes

require

mentstests

44

physics μP

G(z)

H(s)

OS

comms

VM

μP

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACFPGA

network

HDL

physicsμP

G(z)

H(s)

comms FPGA

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACμP

HDL

require

mentstests

require

mentstests

bit

stream

bit

stream

App

OS

VM

App

46

physics μP

G(z)

H(s)

OS

commsμP

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACFPGA

network

HDL

physicsμP

G(z)

H(s)

comms FPGA

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACμP

HDL

require

mentstests

require

mentstests

bit

stream

bit

stream

App

OS

App

middleware

47

physics μP

G(z)

H(s)

OS

commsμP

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACFPGA

network

HDL

physicsμP

G(z)

H(s)

comms FPGA

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACμP

HDL

require

mentstests

require

mentstests

bit

stream

bit

stream

App

OS

App

middleware

48

physics μP

G(z)

H(s)

OS

commsμP

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACFPGA

network

HDL

physicsμP

G(z)

H(s)

comms FPGA

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACμP

HDL

require

mentstests

require

mentstests

bit

stream

bit

stream

App

OS

App

middleware

49

Ab

str

ac

tio

nP

hysic

s (

sta

te)

Ph

ysic

s (

str

uc

ture

)

physics μP

G(z)

H(s)

OS

commsμP

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACFPGA

network

HDL

physicsμP

G(z)

H(s)

comms FPGA

G(s)

C

feature

C++

feature

view1 view2

classes

ADC

DACμP

HDL

require

mentstests

require

mentstests

bit

stream

bit

stream

App

OS

App

middleware

physics

50

Information

Physics

Electronics

Network

51

Physics

Information Information

Physics

ElectronicsElectronics

Network Network

52

Physics

Electronics

Network


Physics

Electronics

Network

53

Physics

Electronics

Network


Physics

Electronics

Network

Open but tightly coupled!

54

Argentinian director Fernando Livschitz of Black Sheep

Films transforms a busy intersection into a choreographed

dance by cloning cars, bikes, and people.

https://youtu.be/ufK2XRGUjuc

55





56

Wireless communication Service utilization

Exploit distributed information

resources

Reliably configure features with

varying quality of service

Assemble available functionality

into features after deployment

Data sharing

Connected

57


resources





Multirate architectures

Extracting and deriving specific

value from general information

Wireless communication Service utilizationData sharing

Connected

Megamodeling and

metamodeling

Technology challenges in

CPS

58


resources








Physically aware configurable

protocol stack that is IP

compatible

Precise timing and

synchronization in a distributed

environment


Connected

IEEE 802.15.4e low cost

communication

IEEE 1588 precise timing Distributed real-time

systems task scheduling

Processor and

network scheduling

59


resources








Physically aware configurable

protocol stack that is IP

compatible

Precise timing and

synchronization in a distributed

environment

Real-time embedded services

operating in a physical

environment

Smart services discovery

Information sharing in a

heterogeneous system ensemble


Connected

Real-time discovery

services

Real-time middleware Semantic middleware

60

Hardware resource sharing Functionality sharing

Reliably configure an ensemble

online to exploit exogenous

functionality

Contract out endogenous

resources and balance use of

exogenous resources

Purpose functionality to create

novel system features post

deployment

Runtime system adaptation

Collaborative

61



functionality



exogenous resources



deployment

Reasoning and planning

adaptation of an ensemble of

systems

Hardware resource sharing Functionality sharingRuntime system adaptation

Collaborative

Models @ runtime Automated model

calibration

62



functionality



exogenous resources



deployment


Collaborative



systems

Flexible and transferable

embedded functionality dispatch

Performance characterization

from abstract functionality

Real-time virtualizationOpen Services Gateway

Initiative (OSGi)

Platform-based design

63



functionality



exogenous resources



deployment


Collaborative



systems

Multi-use functionality post-

deployment

Feature interaction

Flexible and transferable

embedded functionality dispatch

Performance characterization

from abstract functionality

Data distribution

service (DDS)

Online calibrationRequirements mining Multi-rate double

buffering

64

Design artifact sharing

Collaborate between

stakeholders throughout the

system life cycle

Confidently design systems as

part of a reliable ensemble

Virtual system integration

Design

Emerging behavior design

Systematically design optimal

behavior of system ensembles

65

Collaborate between


system life cycle





Proper models in design

System-level design and analysis

by using models

Connectivity among models,

software, and hardware

Design artifact sharingVirtual system integration

Design


Model Building

Automation SystemCounterexample

guided abstraction

refinement

Hybrid dynamic

systems

Multiparadigm modeling Real-time simulationHyperdense time domain

for hybrid bond graphs

66

Collaborate between


system life cycle







by using models



Collaborative planning, guidance,

and control


Design


Service orchestrationService oriented sensor

programming

67

Collaborate between


system life cycle





Tool coupling among disparate

organizations

Support manifold views and tools

in design



by using models



Collaborative planning, guidance,

and control


Design


Graph transformationsSingle underlying modelOpen services for

lifecycle collaboration

68

Pieter J. Mosterman and Justyna Zander, “Cyber-physical

systems challenges: a needs analysis for collaborating

embedded software systems,” in Software & Systems

Modeling, Springer Berlin/Heidelberg, ISSN 1619-1366, vol.

15, nr. 1, pp. 5-16, 2016

69





70

Conclusions

World is becoming machines that

– Adapt to the environment

– Make decisions autonomously

– Are connected

– Work together

Metcalfe is supplanting Moore as

value driver

Key M&S application areas

– Performance

– Concurrency

– Physics

We need a stupendous range of

technologies combined

– Do not be an individualist!

MathWorks touches on most any

of these technologies

Come seek us out to discuss!

– Opportunities

– Solution needs

– Starting points

71

®

Environment models to enable surrogate interactions

Open tool platforms with trusted interfaces for communication

across synchronized and coordinated models, software,

and hardware devices

Efficient simulation models to be used across dynamic and execution semantics

Online model calibration

Maintenance of consistent information and management of inconsistencies regarding the ensemble of systems with sufficient fidelity at runtime

Introspection of the system state, configuration, and services it makes available

Generation of models with necessary detail based on property selection

Connecting, combining, and integrating models represented in different formalisms

Model-based test generation from requirements while preserving the context of dynamic configurations

Setting of initial conditions and injecting fault data

Temporal and spatial partitioning to isolate

functionality for a specific system architecture under

investigation

Information represented as high-level models with well-defined

metamodels and ontologies

Synchronization of data from incongruent sources

Assumption formalization and dependency effect analysis

Online calibration based on objective and performance criteria

Performance characterization via performance models and measures

Generation of models for a desired task perspective by property identification and model behavior selection

Accessible formal methods that apply to collaborative problems

Planning and synthesis of distributed control functionality on concurrent resources

Analysis methods across loosely coupled architectures

Real-time middleware and service oriented architectures with physical capabilities

Service ontologies with taxonomies for similarity and transformability

matching

Language and ontology infrastructure to support

translation and transformation

Scheduling of periodic and aperiodic events with reliable

execution times

Physical layer based timing and synchronization architectures

Real-time services of graded quality with a low footprint and a configurable protocol stack that

includes time and location information

Characterization of computational architectures

Dynamically mixing safety integrity levels

Modeling the semantics of time

Platform-based modeling of execution behavior functionality

Standardized and configurable real-time execution stack

Traceability across semantic and technology adaptation, and intellectual property protection

Information extraction from obfuscated intellectual property

Configurable view projections that are tool specific

Consistent semantics across tools by modeling execution engines

value drivers in a changing landscape of modeling &...

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