A common need in system architecture design is to verify that if the architect is correct and can satisfy its requirements. Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired. However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output, and that is what we called “simulation”. This presentation introduced how to embed Python or MATLAB® codes inside functions to do “simulation” within Capella.

1. Copyright © 2005-2022 PGM All Rights Reserved.
Simulation with Python and MATLAB® in Capella
DESS (Dynamic Execution and System Simulation) add-on
Shanghai PGM Technology Co., Ltd.
v2.0 | 9/3/2022

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Introduction of PGM

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Shanghai PGM Technology Co., Ltd.
PGM is short for Pu Gou Moutain, which is a mountain full of treasure, record in “Shan Hai Jing” (Classic
of Mountains and Rivers, the earliest annals of geography in China, about 2,500 years ago).

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Shanghai PGM Technology Co., Ltd.
PGM is a leading provider of MBSE solution and consulting service in China.
• MBSE training
• MBSE-transition
consulting
• RE training
• Software
development
process consulting
• Implementation
of MBSE solution
• Implementation
of RE solution
• Implementation
of ALM solution
• Support of
MBSE System
• Support of RE
System
• Support of ALM
System
• Co-Design of
complex systems
as MBSE experts
• Review MBSE
models of
complex systems
Consulting IT Implementation IT Support Co-Design

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Our Customers (Part of)

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1. Modeling and transition of the overall requirements model
2. Modeling of ATA system models
3. Integration to the overall integration model
Overall Requirements Model
OA
SA
LA
ATA System Models
LA
PA
SA
Transition
Integration of LA
LA
Integration of PA
PA
Overall Integration Model
Overview Of Our Solution in COMAC

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Overview Of Our Solution in Xiaomi Vehicle

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Background and Motivation

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Motivation
Challenge: how to verify the
architecture design is correct?
• Is the mode and state machine
consistent with scenarios?
• Is the function decomposition
appropriate?
• Can the coordination of
various functions achieve the
desired results?
• …
Consistency ?
Correctness ?

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Solution
ü An extension to make state machine and functional data flow executable. Use a control panel to interact with the excuting
model, and automatically record execution process as a scenario. Judge if the sequence of functions and interactions is
desirable by comparing manual scenarios and auto-record scenarios.
ü During state machine and functional data flow execution, MATLAB® or Python code (Modelica and other simulation languages
under development) embedded in functions can be directly invoked to simulate the operation effect of the architecture.

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Main Features of DESS Add-on
• Execution and animation of statemachines
• Execution of functional date flows
• Auto-generation of scenarios
• System simulation with MATLAB® or Python code embedded in functions
• Export of SCXML based on statemachines

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Execution and Simulation Rules

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Execution Rules of State Machine (1/3)
Based on Mealey machine, as
follows:
• The trigger of transition can be
functional exchange, time event
or change event
• The guard of transition can
directly reference property
values from relative component
• Different state machine can use
“Gen” mechanism to trigger
each other

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Execution Rules of State Machine (2/3)
When transition occurs, the sequence of function
and Gen is as follows:
1. Execute do function of pre-state
2. Generate doGen Trigger of pre-state
3. Trigger arrives
4. Guard of transition can reference property value
5. Execute exit function of pre-state
6. Generate exitGen Trigger of pre-state
7. Execute effect function of transition
8. Generate effectGen Trigger of transition
9. Execute entry function of post-state
10. Generate entryGen Trigger of post-state

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Execution Rules of State Machine (3/3)
Display rules during execution are as follows:
• Current state/mode is highlighted by red
• Transition to current state is highlighted by
yellow
• States/modes and transitions that have been
through are highlighted by grey
• States/modes and transitions that have not
been through display as default
The process of execution will be record as scenarios automatically

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Our extension can support execution of function data flow other
than mode/state machine. The rule is based on Petri-net, as
follows:
• Add initial node, the function connected to initial node will be
executed at first
• The beginning condition of a function’s execution is that all input
ports has at least one token
• The execution of function will consume one token on each input port
• After execution, a function will produce one token on each output
port
• A token will move from the output port to the input port immediately
after it is produced
• The process of execution will be record as scenarios automatically
Execution Rules of Function Data Flow
Execution Execution
Execution

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Execution Rules of the Whole Model
If some components have MSM while others don’t, our extension can make a hybrid-execution of state machine rule
and function data flow rule:
• Components with MSM will execute under state machine rule, and others under function data flow rule
• At the border between state machine rule and function data flow rule, triggers in state machine are equal to tokens in function
data flow

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Control Panel
Users can use control panel to interact with
mode/state machines during execution:
• Select the target to send triggers
• Select the trigger to be sent, and send it
• Define the value of the trigger to be sent ( the
value will be used when relative function has
embedded M code )
• History of triggers that have been sent during
execution

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MATLAB® or Python Code Embedded in Function
Users can invoke MATLAB®/Python editor to edit embedded code by
function’s right-click menu:
• The first several lines of MATLAB®/Python function is auto-generated, re-
using function name and functional input/output
• Property value and exchange item of relative component and function
can be referenced
• Breakpoints can be added in the codes to debug while execution
Users can also create Capella functions by existing simulation codes.

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Display Options for Execution
• The number of functions and interfaces
invoked in execution for a real system
simulation is very large (from thousands to
millions). Sometimes it’s impossible to
highlight MSM and update scenarios in real
time. So we need display options to improve
execution efficiency in some cases.
• If the real-time update option is closed, the
execution process will be record as xml file,
which can be partially inserted into scenario
after execution.

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Export of SCXML
• We can export each statemachine of the model into SCXML files

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Python Example

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Logical Architecture of a Simple System
This simple system has a controller and an engine. The manual order from the
operator will be parsed by the controller and delivered to the engine.

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MSM: Controller
The controller has two
modes: StandBy and
Parsing

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MSM: Engine
The engine has two modes:
Down and Up

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Embedded Python Code for Function parseOrder
Property value from Capella model
can be referenced by “propertyData”
in Python codes.
Exchange Item from Capella model
can also be refenrenced by
“exchangeItemData” in Python codes.

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Execution Effect of Simple Example EngineController_Python操作视频_2.1.mp4 (命令行)

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MATLAB® Example

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Introduction to MPAR Example
• MPAR (Multi-Functional Phased Array Radar)
performs both scanning (searching) and
tracking tasks. Most of its simulation codes
come from MPARSearchTrackExample in
MATLAB® 2019a.
• This example intends to show how to use
DESS to do system simulation. It’s not about
how to design a real phased array Radar. So it
only uses baseband signal, and simplified
signal processing. - This picture comes from MATLAB® 2019a

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Logical Architecture of MPAR
• MPAR consists of five subsystems:
Radar managing subsystem,
antenna subsystem, signal
processing subsystem, data
processing subsystem and display
subsystem. MPAR will search,
confirm and track targets under the
control of simulation configurator.
• Radar managing subsystem,
antenna subsystem and simulation
configurator has MSM, while others
don’t.

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MPAR MSM: Simulation Configurator
• After receiving StartOrder from simulation
controller, simulation configurator will
initialize MPAR simulation environment,
and send initialization signal to different
subsystems.
• When simulation is on, the configurator will
update simulation time at the end of each
dwell.

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MPAR MSM: Radar Managing Subsystem
• After receiving initialization
signal, Radar managing
subsystem will initialize job
queue, and enter working mode.
• In working mode, Radar
managing subsystem will get
current job at the beginning of
each dwell, and provide job
information and beam direction
to other subsystems.

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MPAR MSM: Antenna Subsystem
• After receiving initialization signal, antenna subsystem will initialize the phased array, and enter Stand_By mode.
• In each dwell, antenna subsystem will working through Transmitting mode and Receiving mode for every pulse,
until all pulses are finished.

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Embedded Simulation Code for MPAR Functions (Partial)
getCurrentJob generateDetection updateTrackAndJob

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Execution Effect of MPAR Example MPAR_Demo_v3.0.mp4 (命令行)

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Values of System Simulation by DESS
The consistency between system architecture design and
system simulation is guaranteed.
The granularity of system simulation code is refined to
function level, which is better to maintain and reuse.
It gives engineers a chance to simulate the rough behavior of
system in a very early stage, with no detailed design.

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