Georg Hackenberg

Progress on

Complex Cyber-Physical Systems Engineering

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Question

What is a complex cyber-physical system?

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Answer

The smart grid!

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Answer continued

● Cyber-physical systemA cyber-physical system is a system featuring a tight combination of, and coordination between, the system’s computational and physical elements.

● Complex systemA complex system is a system composed of interconnected parts that as a whole exhibit one or more properties not obvious from the properties of the individual parts.

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Challenges

1Cyber-physical models

● Physical structure● Physical behavior● Computational struct.● Computational behav.

2Emergent properties

● Specification● Refinement● Implementation● Analysis

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Progress

Basic Framework Experimentation Solid Framework Evaluation

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Impressions

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Framework

1Modeling language

● Component hierarchy● Deterministic behavior● Probabilistic behavior● Exploration annotations

2Exploration engine

● Dynamic programming● Alternative generation● Alternative comparison● Space pruning

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Deterministic behavior

(Model

(Observation name:“Temperature“ type:“number“

(Expression

(Sum

(Observation name:“Temperature“ offset:“1“)

(Number value:“1“)

)

)

)

...

)

T (t)=T (t−1)+1

Semantics

Syntax

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P (C (t)= true)=P (C (t)= false)=11+1

=0.5

Probabilistic behavior

(Model

...

(Observation name:“Command“ type:“boolean“

(Distribution

(Option

(Value (Boolean value:“true“))

(Frequency (Number value:“1“))

)

(Option

(Value (Boolean value:“false“))

(Frequency (Number value:“1“))

)

)

)

)

Semantics

Syntax

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Exploration annotations

require

● State verification● True value

equal

● State pruning● Equal value

cost

● State sorting● Lower value

minimize/maximize

● State pruning● Lower/higher value

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Refrigerator example

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Dynamic programming

1. Select– states to follow

2. Generate– following states

3. Calculate– observations

4. Verify– required observations

5. Prune– dominated states

6. Sort– remaining states

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Algorithm complexity

10 refrigerators

15 minute steps

210≈1000 commands per step

210*96≈100096 commands per day

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Complexity reduction

1. Selection● Follow bounded

number of states● Select x% states

according to sorting● Select (100-x)%

states randomly

2. Generation● Generate bounded

number of states● Select options

according to frequency

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Exploration results

-250

-200

-150

-100

-50

0

50

100

150

200

250

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95

-2500

-2000

-1500

-1000

-500

0

500

1000

1500

2000

2500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95

-1500000

-1000000

-500000

0

500000

1000000

1500000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95

1 refrigerator

10 refrigerators

5000 refrigerators

0

5

10

15

20

25

30

35

40

45

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93

0

0,2

0,4

0,6

0,8

1

1,2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93

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Exploration results continued

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Open questions● Is the language sufficient?

– What about dynamic structure?– What about property specification?– What about exploration guidance?

● Is the engine sufficient?– What about linear programming?– What about genetic programming?– What about machine learning?

● Is the toolbox operationalizable?

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Outlook

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Outlook continued● Software Campus Iteration

– Presenting results– Evaluating toolbox– Discussing evolution

● Analyze models with respect to ...– Communication overhead– Failure robustness– Attack robustness

● Extend models with respect to …– Mobility requirements– Business requirements– Engineering perspectives

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The end.