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Ivan Ruchkin, Selva Samuel, Bradley Schmerl,
Amanda Rico, and David Garlan
Institute for Software Research, Carnegie Mello...
2
3
 CPS operate in uncertain contexts
 Need to adapt to unanticipated situations
4
System & environment
under adaptation
Adaptation with models
Phenomena
5
System & environment
under adaptation
Adaptation with physical models
Physical phenomena
6
NUC
(computer)
Kinect
(sensor)
Base
(actuator
& battery)
7
8
 Abstractions of physical objects and
interactions
 Beyond simple discrete models
 Objects may be in the system, in the...
 Software models guide state-of-the-art
adaptive systems
 Physical models are often implicit or assumed
 In CPS, we nee...
1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
11
1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
12
 Many formalisms and tools are available for
modeling CPS
 Differential equations, signal flow graphs, automata
 Positi...
 Evaluate individual formalisms
 Expressiveness
▪ Linear/non-linear, continuous/discrete, classes of
functions (polynomi...
 We chose a linear real-valued regression model
 Continuous changes in parameters
 Easily embeddable into other models
...
1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
16
 Goal: maximize value of each model
 Analytical power: strength of predictions and
explanations
 Fragility: amount of r...
 Theory-driven
 Physical theory
dictates first principles
 Calibrate with data
18
 Data-driven
 Collect data first
 ...
19
 We chose to use data-driven approach
 Low expertise with theory-driven models
 Ok with low-precision far-horizon pr...
1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
20
 Software models in adaptation are used for:
 State estimation and prediction
 Triggering adaptive changes
 Choosing a...
 Clear representation
 Either separate models or explicit embedding
 Easier change and reuse
 Coordinated use with cyb...
Physical models in adaptive CPS are
important and difficult to build
23
Challenge Position
Selecting modeling
formalism
Em...
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Challenges in Physical Modeling for Adaptation of Cyber-Physical Systems

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The initial version of slides is due to Selva Samuel.

Abstract: "Cyber-physical systems (CPSs) mix software, hardware, and physical aspects with equal importance. Typically, the use of models of such systems during run time has concentrated only on managing and controlling the cyber (software) aspects. However, to fully realize the goals of a CPS, physical models too have to be treated as first-class models. This approach gives rise to three main challenges: (a) identifying and integrating physical and software models with different characteristics and semantics; (b) obtaining instances of physical models at a suitable level of abstraction for adaptation; and (c) using and adapting physical models to control CPSs. In this position paper, we elaborate on these three challenges and describe our vision of making physical models first-class entities in adaptation. We illustrate this vision in the context of power adaptation for a service robotic system."

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Challenges in Physical Modeling for Adaptation of Cyber-Physical Systems

  1. 1. Ivan Ruchkin, Selva Samuel, Bradley Schmerl, Amanda Rico, and David Garlan Institute for Software Research, Carnegie Mellon University
  2. 2. 2
  3. 3. 3  CPS operate in uncertain contexts  Need to adapt to unanticipated situations
  4. 4. 4 System & environment under adaptation Adaptation with models Phenomena
  5. 5. 5 System & environment under adaptation Adaptation with physical models Physical phenomena
  6. 6. 6 NUC (computer) Kinect (sensor) Base (actuator & battery)
  7. 7. 7
  8. 8. 8
  9. 9.  Abstractions of physical objects and interactions  Beyond simple discrete models  Objects may be in the system, in the environment, or on the border  Example: power model forTurtleBot  How much does each task consume?  How much power is left given current voltage?  How long does it take to charge? 9
  10. 10.  Software models guide state-of-the-art adaptive systems  Physical models are often implicit or assumed  In CPS, we need both software and physical models! 10
  11. 11. 1. Selecting modeling formalism 2. Obtaining physical models 3. Using physical models in adaptation 11
  12. 12. 1. Selecting modeling formalism 2. Obtaining physical models 3. Using physical models in adaptation 12
  13. 13.  Many formalisms and tools are available for modeling CPS  Differential equations, signal flow graphs, automata  Position: no single formalism is enough to model adaptive CPS; we need to embrace their multiplicity 13
  14. 14.  Evaluate individual formalisms  Expressiveness ▪ Linear/non-linear, continuous/discrete, classes of functions (polynomials, transcendental functions, etc.)  Types of analyses supported ▪ Trade-off between expressiveness and computing cost  Engineering expertise ▪ Novices: higher effort and lower quality  We need approaches to integrate formalisms!  Difficult problem, outside of talk’s scope 14
  15. 15.  We chose a linear real-valued regression model  Continuous changes in parameters  Easily embeddable into other models 15 P(v, t) = Av + Bt + C 15 time (s) power(wh)
  16. 16. 1. Selecting modeling formalism 2. Obtaining physical models 3. Using physical models in adaptation 16
  17. 17.  Goal: maximize value of each model  Analytical power: strength of predictions and explanations  Fragility: amount of rework to accommodate future changes  Computational cost: amount of processing needed for analysis  Position: the way we build physical models affects their value.We need more guidance! 17
  18. 18.  Theory-driven  Physical theory dictates first principles  Calibrate with data 18  Data-driven  Collect data first  Then create abstractions from it time (s) power(wh)
  19. 19. 19  We chose to use data-driven approach  Low expertise with theory-driven models  Ok with low-precision far-horizon predictions  The model is fragile: hard to change
  20. 20. 1. Selecting modeling formalism 2. Obtaining physical models 3. Using physical models in adaptation 20
  21. 21.  Software models in adaptation are used for:  State estimation and prediction  Triggering adaptive changes  Choosing adaptive strategy  + Continuous improvement of models themselves  Position: physical models should also be treated as first-class entities in adaptation 21
  22. 22.  Clear representation  Either separate models or explicit embedding  Easier change and reuse  Coordinated use with cyber models  Estimation, prediction, choice  Models themselves should be adapted  Model value & cost should be the guiding factors  Need to reason about model value at run time! 22
  23. 23. Physical models in adaptive CPS are important and difficult to build 23 Challenge Position Selecting modeling formalism Embrace multiplicity; use formalisms based on expressiveness, analyses, and expertise. Obtaining physical models Model value should the guiding factor. More guidance is needed to connect model- building and model value. Using physical models in adaptation Physical models should be treated as first- class entities and adapted based on their value at run time.

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