Institute for Software Integrated Systems
Vanderbilt University
CYBER PHYSICAL SYSTEMS (CPS)
Janos Sztipanovits
ISIS, Vanderbilt University
Overview
 What are Cyber-Physical Systems?
 Scientific challenges
 Composition and compositionality
 How to build, compose networked CPS at all scales?
 How to achieve compositionality in high-confidence, dynamically-configured systems?
 Design and design automation
 What is the meaning and cost of heterogeneity?
 How to accommodate changes in products, design process and design
culture
 Interaction-based computing
 What are the new computing paradigms?
 How to model interactions
2
CPS: Computing Perspective
• Two types of computing systems •
– Desktops, servers, PCs, and
notebooks
– Embedded
• The next frontier
– Mainframe computing (60’s-70’s)
• Large computers to execute big
data processing applications
– Desktop computing & Internet (80’s90’s)
• One computer at every desk to do
business/personal activities
– Embedded computing (21st
Century)
• “Invisible” part of the
environment
• Transformation of industry
Number of microprocessor
units per year
– Millions in desktops
– Billions in embedded processors
•
Applications:
– Automotive Systems
• Light and heavy automobiles,
trucks, buses
– Aerospace Systems
• Airplanes, space systems
– Consumer electronics
• Mobile phones, office electronics,
digital appliances
– Health/Medical Equipment
• Patient monitoring, MRI, infusion
pumps, artificial organs
– Industrial Automation
• Supervisory Control and Data
Acquisition (SCADA) systems for
chemical and power plants
• Manufacturing systems
– Defense
• Source of superiority in all
weapon systems
3
CPS: Systems Perspective
Sectors
Transportation
Defense
Energy and
Industrial
Automation
Opportunities
Aircraft that fly faster and further on
less energy. Air traffic control
systems that make more efficient
use of airspace.
Automobiles that are more capable
and safer but use less energy.
More capable defense systems;
defense systems that make better use
of networked fleets of autonomous
vehicles.
New and renewable energy sources.
Homes, office, buildings and
vehicles that are more energy
efficient and cheaper to operate.
CPS Definition
A CPS is a system in which:
 information processing and physical processes
are so tightly integrated that it is not possible to
identify whether behaviors are the result of
computations, physical laws, or both working
together
 where functionality and salient system
characteristics are emerging through the
interaction of physical and computational objects
Transformation of Industries:
Automotive

Current picture
 Largely single-vehicle focus
 Integrating safety and fuel economy (full hybrids,
regenerative braking, adaptive transmission control,
stability control)
 Safety and convenience “add-ons” (collision
avoidance radar, complex airbag systems, GPS, …)
 Cost of recalls, liability; growing safety culture

Better future?
 Multi-vehicle high-capacity cooperative control
roadway technologies
 Vehicular networks
 Energy-absorbing “smart materials” for collision
protection (cooperative crush zones?)
 Alternative fuel technologies, “smart skin”
integrated photovoltaics and energy scavaging, ….
 Integrated operation of drivetrain, smart tires,
active aerodynamic surfaces, …
 Safety, security, privacy certification; regulatory
enforcement

Time-to-market race
Image thanks to Sushil Birla, GMC
6
Transformation of Industries:
Health Care and Medicine
 National Health Information Network,
Electronic Patient Record initiative
 Medical records at any point of service
 Hospital, OR, ICU, …, EMT?
 Home care: monitoring and control
 Pulse oximeters (oxygen saturation), blood glucose
monitors, infusion pumps (insulin), accelerometers
(falling, immobility), wearable networks (gait
analysis), …
 Operating Room of the Future (Goldman)
 Closed loop monitoring and control; multiple
treatment stations, plug and play devices; robotic
microsurgery (remotely guided?)
 System coordination challenge
 Progress in bioinformatics: gene, protein
expression; systems biology; disease
dynamics, control mechanisms
Images thanks to Dr. Julian Goldman, Dr. Fred Pearce
7
Transformation of Industries:
Electric Power Grid
 Current picture:
 Equipment protection devices trip
locally, reactively
 Cascading failure: August (US/Canada)
and October (Europe), 2003
 Better future?
 Real-time cooperative control of
protection devices
 Or -- self-healing -- (re-)aggregate
islands of stable bulk power (protection,
market motives)
 Ubiquitous green technologies
 Issue: standard operational control
concerns exhibit wide-area
characteristics (bulk power stability and
quality, flow control, fault isolation)
 Context: market (timing?) behavior,
power routing transactions, regulation
Images thanks to William H. Sanders, Bruce Krogh, and Marija Ilic
IT Layer
8
Why is CPS Hard?
Software
Control
Systems
package org. apac he.to mcat. sessi on;
import org.a pach e.tom cat.c ore.* ;
import org.a pach e.tom cat.u til.S tring Mana ger;
import java. io.* ;
import java. net. *;
import java. util .*;
import javax .ser vlet. *;
import javax .ser vlet. http. *;
/**
* Core impl emen tatio n of a ser ver s essi on
*
* @aut hor J ames Dunc an Da vidso n [du ncan @eng. sun.c om]
* @aut hor J ames Todd [gon [email protected] g.sun .com ]
*/
public class Ser verSe ssion {
pri vate Stri ngMan ager sm =
Stri ngMa nager .getM anage r("or g.ap ache. tomca t.ses sion" );
pri vate Hash table valu es = new H asht able( );
pri vate Hash table appS essio ns = new Hasht able( );
pri vate Stri ng id ;
pri vate long crea tionT ime = Syst em.c urren tTime Milli s();;
pri vate long this Acces sTime = cr eati onTim e;
pri vate long last Acces sed = crea tion Time;
pri vate int inact iveIn terva l = - 1;
Ser verSe ssio n(Str ing i d) {
this .id = id;
}
pub lic S trin g get Id() {
retu rn i d;
}
pub lic l ong getCr eatio nTime () {
retu rn c reati onTim e;
}
pub lic l ong getLa stAcc essed Time( ) {
retu rn l astAc cesse d;
}
pub lic A ppli catio nSess ion g etApp lica tionS essio n(Con text cont ext,
bool ean creat e) {
Appl icat ionSe ssion appS essio n =
(App licat ionSe ssion )appS essi ons.g et(co ntext );
if ( appS essio n == null && cr eate ) {
// X XX
// s ync t o ens ure v alid?
appS essio n = n ew Ap plica tion Sessi on(id , thi s, co ntex t);
appS essio ns.pu t(con text, app Sessi on);
}
// X XX
// m ake sure that we ha ven't gon e ove r the end of ou r
// i nact ive i nterv al -- if s o, i nvali date and c reate
// a new appS essio n
retu rn a ppSes sion;
}
voi d rem oveA pplic ation Sessi on(Co ntex t con text) {
appS essi ons.r emove (cont ext);
}
/**
* Calle d by cont ext w hen r eques t co mes i n so that acces ses and
* inact ivit ies c an be deal t wit h ac cordi ngly.
*/
voi d acc esse d() {
// s et l ast a ccess ed to this Acce ssTim e as it wi ll be lef t ove r
// f rom the p revio us ac cess
last Acce ssed = thi sAcce ssTim e;
this Acce ssTim e = S ystem .curr entT imeMi llis( );
}
voi d val idat e()
Crosses Interdisciplinary Boundaries
• Disciplinary boundaries need to be realigned
• New fundamentals need to be created
• New technologies and tools need to be developed
• Education need to be restructured
9
Long-Term Goal
 Transform how we interact with the
physical world just like the internet
transformed how we interact with one another.
 Transcend space
 Control the physical environment remotely
 Building CPSs that integrate computational and
physical objects requires new systems science
foundations.
 Fusion of physical and computational sciences
Produce significant impact on society and national
competitiveness.
Overview
 What are Cyber-Physical Systems?
 Scientific challenges
 Composition and compositionality
 How to build, compose networked CPS at all scales?
 How to achieve compositionality in high-confidence, dynamically-configured
systems?
 Design and design automation
 What is the meaning and cost of heterogeneity?
 How to accommodate changes in products, design process and design
culture
 Interaction-based computing
 What are the new computing paradigms?
 How to model interactions
11
Example:
CPS Composition Theories 1/3
v(t) = R * i(t)
v(t) = R * i(t - t)
R/r is the pole of
the transfer function
v(z)/e(z) => the step
response may become
unstable.
Source: Roitman and Diniz, ICDS’95, 1995
Example:
CPS Composition Theories 2/3
Discretization of components
using trapezodial rule for
integration.
Composition task:
Source: Bilbao and Smith III, 2005
Not realizable implementation..
Example:
CPS Composition Theories 3/3
 Wave Digital Filters
Alfred Fettweis in the early 1970s, was an attempt at translating analog filters into the
digital realm with a pointed emphasis on preserving as much of the underlying physics as
possible.
Properties:
–
–
–
–
if the reference filter is passive, the WDF implementation is also passive
guaranteed stability
reduced accuracy requirements for coefficients and good dynamic range
free of zero input limit-cycle oscillation, …
 Resonator-Bank Filters
Peceli and others, 1980’s. Similar properties, different implementation strategy.
Need for investigating composition
theories for CPS
Overview
 What are Cyber-Physical Systems?
 Economic context
 Scientific challenges
 Composition and compositionality
 How to build, compose networked CPS at all scales?
 How to achieve compositionality in high-confidence, dynamically-configured systems?
 Design and design automation
 What is the impact and cost of heterogeneity?
 How to accommodate changes in products, design process and
design culture
 Interaction-based computing
 What are the new computing paradigms?
 How to model interactions
15
Heterogeneity and Modeling
Languages
Computing
System
Composition
Domain
Physical
instantiation
Logical
specification
(source code)
Physical
system
characteristics
• “Cyber” Models
• Modeling Languages
– Structure
– Behaviors
• Mathematical Domains
– traces/state variables
– no reference semantics
or “semantic units”
Physical
System
Composition
Domain
• Physical Models
• Modeling Languages
– Structure
– Behaviors
• Physical Laws
– Physical variables
– Physical Units
Challenges in DSML Semantics
DSML
?
Semantic
Domain
• Usually, specification stops at the level of abstract
syntax metamodels (“static semantics”)
• Specification of behavioral semantics (if done)
– involve major effort due to overly complex
modeling languages,
– use a wide range of formalisms and
• Impact is far-reaching
– tool chains are closed and built around
loosely defined “conventions” and proprietary
interpretations of semantics instead of
standards
– potential semantic mismatches create
unacceptable risk for safety critical applications
Major roadblock that slows down acceptance of
model-based design technology.
Need for developing theories and tools for compositional
specification of DSML semantics
Goal: Heterogeneous and
Composable Design Flows
Modeling
Controller
Synthesis
System
Analysis
Code
Synthesis
Validation
Verification
Target
Analysis
Platform
Comp/Platf
Modeling
Component
Implement.
System
Modeling
Valid Model
Target
Analysis
Platform
Interaction/Fault mgmt/… Models
Test Vectors
Integrated Model
Validation
Verification
Platform Models
Platform Models
Plant Model
Component
Integration
Code/Model
Valid Code/Model
Download
Component Model
Partial Model
System Model
Integrated Code
Model
Component Code
Download
Valid Code/Model
Design Feedback
Valid Model
Design Feedback
Design Feedback
Design Feedback
Design Feedback
Simulink
Stateflow
ECSL/GME
Ptolemy
Checkmate
Charon
BACKPLANE
Metagenerators
Metamodel
Composition &
Validation
Metamodeling
Matlab
Simulator
Checkmate
SAL
Teja
UP Reach
Charon
R-T
Workshop
ECSL/GME
Kestrel
Ptolemy
Checkmate
AIRES
WindView
AIRES
MPC555/
OSEK
PENTIUM/
QNX
Automotive Design Flow
Open Tool Integration Framework
MIC/GEN
Kestrel
GME/Meta
UML/OCL
GME/Meta
UML/Rose
ESML/GME
Manual
ESML/GME
Honeywell
CMU
ESML/GME
Honeywell
TimeWeaver
AIRES
SWRI/ASC
TimeWiz
AIRES
SWRI/ASC
ESML/GME
PENTIUM/
TAO/
BOLDSTROKE
Avionics Design Flow
• Integrated Physical/Computational
Modeling and Analysis
• Generative Programming
• Hybrid System Analysis
• Customizable (metaprogrammable)
modeling tools and generators
• Open tool integration framework;
configurable design flow and
composable design environments
Overview
 What are Cyber-Physical Systems?
 Economic context
 Scientific challenges
 Composition and compositionality
 How to build, compose networked CPS at all scales?
 How to achieve compositionality in high-confidence, dynamically-configured systems?
 Design and design automation
 What is the meaning and cost of heterogeneity?
 How to accommodate changes in products, design process and design
culture?
 Interaction-based computing
 What are the new computing paradigms?
 How to model interactions?
19
Change in Computing Platforms
Future Computers on Silicon
• 1 Power Processor Element (PPE)
• 8 Synergistic Processor Element (SPE)
• Interconnection (EIB) consists of 4 x 16
byte rings run at half the CPU clock speed
and can allow up to 3 simultaneous
transfers. Theoretical peak of the EIB is
204.8 Gigabytes per second.
• Programming challenge – coordinating
the execution of jobs….
• Programming models:
- job queue,
- multitasking,
- stream processing,
-…
• Challenge: understanding task interaction
and utilizing concurrency
Change in CPS Applications:
Networked Systems
Future Systems in the Field
ESO
U ser M anagem ent
S o ftw a r e U p g r a d e
R e m o te T r o u b le s h o o t
R e m o te S e r v e r M g t
S o ftw a r e D is tr ib u tio n
S o ftw a r e In s ta ll
D is p o s a l
Ad m inistration Application s
S ys te m M a n a g e m e n t
L o g is tic s
P e rs o n n e l
T r a n s p o r ta tio n
E n g in e e r in g
P ro c u re m e n t
F a c ilitie s
In te g r a te d S u s ta in m e n t
B usiness Applications
E m b e d d e d M is s io n T r a in in g
B a ttle C o m m a n d
T a r g e t R e c o g n itio n
S e n s o r F u s io n
M is s io n P la n n in g & P r e p
M ission Applications
S itu a tio n U n d e r s ta n d in g
H yd r a u lic
E le c tr ic a l
P r o p u ls io n
N a v ig a tio n
F u e l S ys
Distributed Database
Information Layer
H e a lth M a n a g e m e n t
Vehicle Applications
Interoperable
export
HQ
C O TS
NDI
Ap plication Program Interfaces – C om m on Services
SO S O perations Services
Inform ation Assurance (IA)
N etw ork M gt (NM )
Inform ation D issem ination M gt (ID M )
SO S Fram ew ork Services
COP
N etw ork Infrastructure Services
C O TS
NDI
O perating S ystem Abstractio n Services
O perating S ystem
Foundation Infrastructure – (e.g, N etw ork w ith: C O M SEC C rypto Services, M obility Enh ancem e nts, IP N etw ork Appliqué's, )
Warfighter Interface
XX
Embedded Training
Integrated Sustainment
Target Recognition
Sensor Fusion
EPLRS
Link 4A
SINCGARS Link 11
VHF
Link 16
WIN T
Reachback
Battle Mgmt & Execution
Interoperability
Situation Understanding
FIOP
Mission Planning & Prep
DB Synchronization
HHQ
• Heterogeneous CPS
H um an M achine Interface /M achine -M achine Interface
C o n tr o ls
Joint Common
Database
Standards-Based
Open Software
Architecture
Information Management
Common Operating
Picture
Common Services
Battle
Command
Information Management
• Open Dynamic
Architecture
- heterogeneous
networking
- heterogeneous
components
Computing and Networking
ESO
HQ
UE/HQ
WIN-T
• Very high level
concurrency with
complex interactions
Hierarchical Ad-Hoc Network
WNW
WNW
stubnet
EO/IR
JTRS
Data
Images
Voice
Video
UGS
EO/IR
SAR/MTI
Networked Command
L COP
L COP
Vetronics
L COP
Common Vehicle
Subsystems
L COP
Platform
• Challenge: understanding
system interactions
and analyzing (bounding)
behavior
Interaction and Coordination
Changes in Cyber
Changes in Physical
 Rich time models instead of
sequencing
 Behavioral invariants instead
of end results
 Functionality through
interactions of ongoing
behaviors instead of sequence
of actions
 Component architectures
instead of procedural
abstraction
 Concurrency models with
partially ordered instead of
linearly ordered event sets
 Precise interaction and
coordination protocols
 Hugely increased system
size with controllable, stable
behavior
 Dynamic system
architectures (nature and
extent of interaction can be
modified)
 Adaptive, autonomic
behavior
 Self-descriptive, self
monitoring system
architecture for safety
guarantees.
Summary
 CPS-s represent the coming new age in systems
design
 The required technology changes are
fundamental – go way beyond “multidisciplinary”
design
 The impact on competitiveness is huge: CPS-s
are the foundation for the systems industry
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Cyber Physical Systems - Institute for Software Integrated