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Cyber-Physical Systems, A Fundamental Intellectual ChallengeEdward A. Lee
Citation
Edward A. Lee. "Cyber-Physical Systems, A Fundamental Intellectual Challenge". Talk or presentation, 11, December, 2013; Invited Talk, College de France, Paris, France.
Abstract
The term cyber-physical systems (CPS) refers to the integration of computation and networking with physical processes. CPS is firmly established as a buzzword du jour. Yet many of its elements are familiar and not altogether new. Is CPS just a rehash of old problems designed to attract new funding? In this talk, I will argue that quite to the contrary, CPS is pushing hard at the frontiers of engineering knowledge, putting severe stress on the abstractions and techniques that have proven so effective in the separate spaces of cyber systems (information and computing technology) and physical systems (the rest of engineering). My argument will center on the role of models, and I will show that questions about semantics of models become extremely challenging when the models are required to conjoin the cyber and the physical worlds.
A key challenge is that the notion of dynamics differs in the engineering abstractions used for the cyber and physical sides of the problem. On the physical side, dynamics refers to the change of state of a system over time, where time plays a central role. Many of the core abstractions used in the engineering of such systems explicitly refer to time. For example, ordinary differential equations (ODEs) are frequently used to describe the motion of mechanical parts and the dynamics of electrical circuits. In contrast, on the cyber side, the notions of computation dating back to Turing and Church make no reference to time, modeling dynamics as sequences of discrete state changes. These abstractions are fundamentally algorithmic, step-by-step operations, where the time it takes to perform a step is irrelevant.
The engineering abstractions used in both the cyber and physical spaces are key enablers of the high-tech revolution of the 20th century. On the cyber side, the ability to execute algorithms repeatedly, quickly, and essentially flawlessly underlies much of the information technology revolution. On the physical side, the ability to design stable and robust control systems accounts for the extraordinary reliability and efficiency of vehicles and transportation systems.
A central feature of these abstractions is determinism, where, once the inputs are defined, the behavior of a model of the system is unique and well-defined. Exactly one behavior is correct. Such determinism makes these models very powerful, because analyses of the models acquire the strength of mathematical theorems. Moreover, the models correspond well with the actual behavior of physical realizations. For example, a modern microprocessor can correctly execute a program with extremely high reliability. A mechanical feedback coupling can closely emulate the ODEs used to model it. The combination of expressiveness of the models, the fidelity of the models to the physical realization, and determinism is extremely powerful.
But when cyber and physical abstractions are combined, with today's abstractions, we lose determinism. The interaction between an algorithm and a physical dynamics is not well defined, because the modeling semantics of the algorithms eschews time, whereas the modeling semantics of the ODE embraces time. So instead of building models with deterministic abstractions, engineers today build cyber-physical systems by separately designing the cyber and physical parts, and then discovering the dynamics when they put the two realizations together.
To solve this problem, we can endow the cyber parts with physical abstractions (cyberizing the physical), for example by introducing time in the semantics. Or we can endow the physical parts with cyber abstractions (physicalizing the cyber), for example by enabling database queries over sensor networks. Both approaches have value and are necessary for the full realization of the vision of cyber-physical systems.
Electronic downloads
- HTML
Edward A. Lee. <a
href="http://chess.eecs.berkeley.edu/pubs/1045.html"
><i>Cyber-Physical Systems, A Fundamental
Intellectual Challenge</i></a>, Talk or
presentation, 11, December, 2013; <i>Invited
Talk</i>, College de France, Paris, France.
- Plain text
Edward A. Lee. "Cyber-Physical Systems, A Fundamental
Intellectual Challenge". Talk or presentation, 11,
December, 2013; <i>Invited Talk</i>, College de
France, Paris, France.
- BibTeX
@presentation{Lee13_CyberPhysicalSystemsFundamentalIntellectualChallenge,
author = {Edward A. Lee},
title = {Cyber-Physical Systems, A Fundamental Intellectual
Challenge},
day = {11},
month = {December},
year = {2013},
note = {<i>Invited Talk</i>, College de France, Paris,
France.},
abstract = {The term cyber-physical systems (CPS) refers to
the integration of computation and networking with
physical processes. CPS is firmly established as a
buzzword du jour. Yet many of its elements are
familiar and not altogether new. Is CPS just a
rehash of old problems designed to attract new
funding? In this talk, I will argue that quite to
the contrary, CPS is pushing hard at the frontiers
of engineering knowledge, putting severe stress on
the abstractions and techniques that have proven
so effective in the separate spaces of cyber
systems (information and computing technology) and
physical systems (the rest of engineering). My
argument will center on the role of models, and I
will show that questions about semantics of models
become extremely challenging when the models are
required to conjoin the cyber and the physical
worlds. <p> A key challenge is that the notion of
dynamics differs in the engineering abstractions
used for the cyber and physical sides of the
problem. On the physical side, dynamics refers to
the change of state of a system over time, where
time plays a central role. Many of the core
abstractions used in the engineering of such
systems explicitly refer to time. For example,
ordinary differential equations (ODEs) are
frequently used to describe the motion of
mechanical parts and the dynamics of electrical
circuits. In contrast, on the cyber side, the
notions of computation dating back to Turing and
Church make no reference to time, modeling
dynamics as sequences of discrete state changes.
These abstractions are fundamentally algorithmic,
step-by-step operations, where the time it takes
to perform a step is irrelevant. <p> The
engineering abstractions used in both the cyber
and physical spaces are key enablers of the
high-tech revolution of the 20th century. On the
cyber side, the ability to execute algorithms
repeatedly, quickly, and essentially flawlessly
underlies much of the information technology
revolution. On the physical side, the ability to
design stable and robust control systems accounts
for the extraordinary reliability and efficiency
of vehicles and transportation systems. <p> A
central feature of these abstractions is
determinism, where, once the inputs are defined,
the behavior of a model of the system is unique
and well-defined. Exactly one behavior is correct.
Such determinism makes these models very powerful,
because analyses of the models acquire the
strength of mathematical theorems. Moreover, the
models correspond well with the actual behavior of
physical realizations. For example, a modern
microprocessor can correctly execute a program
with extremely high reliability. A mechanical
feedback coupling can closely emulate the ODEs
used to model it. The combination of
expressiveness of the models, the fidelity of the
models to the physical realization, and
determinism is extremely powerful. <p> But when
cyber and physical abstractions are combined, with
today's abstractions, we lose determinism. The
interaction between an algorithm and a physical
dynamics is not well defined, because the modeling
semantics of the algorithms eschews time, whereas
the modeling semantics of the ODE embraces time.
So instead of building models with deterministic
abstractions, engineers today build cyber-physical
systems by separately designing the cyber and
physical parts, and then discovering the dynamics
when they put the two realizations together. <p>
To solve this problem, we can endow the cyber
parts with physical abstractions (cyberizing the
physical), for example by introducing time in the
semantics. Or we can endow the physical parts with
cyber abstractions (physicalizing the cyber), for
example by enabling database queries over sensor
networks. Both approaches have value and are
necessary for the full realization of the vision
of cyber-physical systems. },
URL = {http://chess.eecs.berkeley.edu/pubs/1045.html}
}
Posted by Mary Stewart on 2 Jan 2014.
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