Using Finite State Machines From Concept to Realization

transcript

Mission Simulation LabHICEE

Profile: Name: Using Finite State Machines From Concept to RealizationPlace: INPEDate: 2017-10-31 – 16h00 18h00 and 2017-11-07 – 15h30 18h00Target Audience: MSc/PhD Students

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Using Finite State Machines FromConcept to Realization

Christopher Shneider Cerqueira Ana Maria Ambrosio Claudio Kirner

Mission Simulation LabHICEEMotivation

•Complex systems are often reactive, i.e., they continuously respond to external and internal stimuli (events) and may have time constraints.

•When modelling such systems, the designer should be able to determine the system's behaviour, as well as its flow of control.

•System modelling methods must express this action semantics in a way that is both formal and intuitive.

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Mission Simulation LabHICEEAutomata theory

• A deterministic finite automaton isrepresented formally by a 5-tuple<Q,Σ,δ,q0,F>, where:• Q is a finite set of states.

• Σ is a finite set of symbols, called thealphabet of the automaton.

• δ is the transition function, that is,δ: Q × Σ → Q.

• q0 is the start state, that is, the state ofthe automaton before any input hasbeen processed, where q0∈ Q.

• F is a set of states of Q (i.e. F⊆Q) calledaccept states.

08:20cscerqueira.com.br 4https://en.wikipedia.org/wiki/Automata_theory

Mission Simulation LabHICEEFinite-State Machine

• A finite-state machine (FSM)or finite-state automaton (FSA,plural: automata), finiteautomaton, or simply a statemachine, is a mathematicalmodel of computation. It isan abstract machine that can bein exactly one of a finite numberof states at any given time. TheFSM can change from one state toanother in response to someexternal inputs; the change fromone state to another is calleda transition. An FSM is defined bya list of its states, its initial state,and the conditions for eachtransition.

Current State

Input Next State Output

Lockedcoin Unlocked

Unlocks the turnstile so that the customer can push through.

push Locked None

Unlocked

coin Unlocked None

push LockedWhen the customer has pushed through, locks the turnstile.

https://en.wikipedia.org/wiki/Finite-state_machine

Mission Simulation LabHICEEMoore Machine

• The FSM uses only entry actions,i.e., output depends only on thestate. The advantage of the Mooremodel is a simplification of thebehaviour. Consider an elevatordoor. The state machine recognizestwo commands: "command_open"and "command_close", whichtrigger state changes. The entryaction (E:) in state "Opening" startsa motor opening the door, the entryaction in state "Closing" starts amotor in the other direction closingthe door. States "Opened" and"Closed" stop the motor when fullyopened or closed. They signal tothe outside world (e.g., to otherstate machines) the situation: "dooris open" or "door is closed"

Mission Simulation LabHICEEMealy Machine

• The FSM also uses input actions,i.e., output depends on input andstate. The use of a Mealy FSM leadsoften to a reduction of the numberof states. The example in Figureshows a Mealy FSM implementingthe same behaviour as in theMoore example. There are twoinput actions (I:): "start motor toclose the door if command_closearrives" and "start motor in theother direction to open the door ifcommand_open arrives". The"opening“ and "closing"intermediate states are not shown.

Mission Simulation LabHICEEWhere can we find FSMs!?

08:20cscerqueira.com.br 8Idealization / Realization Tests Operations

Mission Simulation LabHICEETools

• OPCat: • http://esml.iem.technion.ac.il

• Arcadia: • https://polarsys.org/capella/

• Enterprise Architect: • http://sparxsystems.com/products/ea/

• MatLab: • https://www.mathworks.com/downloads/

• Arduino: • https://www.arduino.cc

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MBSE – 20min ALFA – 20minOPM – 40minARCADIA – 50minCode – 10minMATLAB – 30minARDUINO– 10minWRAP-UP– 10min

Model Based System Engineering ( MBSE)

08:20cscerqueira.com.br 11https://mediaex-server.larc.nasa.gov/Academy/Play/804f524f16c24fd2927a4eb9f01bdf831d

Mission Simulation LabHICEESystem Engineering

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“Systems Engineering is an engineering discipline whose responsibility is creating and executing an interdisciplinary process to

ensure that the customer and stakeholder's needs are satisfied in a high quality, trustworthy, cost efficient and schedule compliant manner

throughout a system's entire life cycle.”

http://www.incose.org/AboutSE/WhatIsSE

Mission Simulation LabHICEEHow the magic happens?

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Talk with Stks

Mission Simulation LabHICEEEngineered System or System Engineering

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An engineered system is an open system of technical or

sociotechnical elements that exhibits emergent properties notexhibited by its individual elements. It is created by and forpeople; has a purpose, with multiple views; satisfies keystakeholders’ value propositions; has a life cycle and evolutiondynamics; has a boundary and an external environment; and ispart of a system-of-interest hierarchy.

Systems engineering is “an interdisciplinary approach and

means to enable the realization of successful (engineered)systems”. It focuses on holistically and concurrentlyunderstanding stakeholder needs; exploring opportunities;documenting requirements; and synthesizing, verifying,validating, and evolving solutions while considering the completeproblem, from system concept exploration through systemdisposal.

http://sebokwiki.org/wiki/Use_Case_0:_Systems_Engineering_Novices

Mission Simulation LabHICEEMBSE

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“Model-Based Systems Engineering (MBSE) is a systems engineering methodology that focuses on

creating and exploiting domain models as the primary means of information exchange between

engineers, rather than on document-based information exchange.”

http://www.omgwiki.org/MBSE/doku.php

“the formalized application of modelling to support system requirements, design, analysis, verification and validation activities beginning inthe conceptual design phase and continuing throughout development and later life cycle phases. MBSE is part of a long-term trend towardmodel-centric approaches adopted by other engineering disciplines, including mechanical, electrical and software. In particular, MBSE isexpected to replace the document-centric approach that has been practiced by systems engineers in the past and to influence the futurepractice of systems engineering by being fully integrated into the definition of systems engineering processes.”

Mission Simulation LabHICEEModel (driven x based x centric x oriented)

• Driven: “models should be used to directly generate executable systems”

• Based: “uses models rather than documents as the data source for all activities throughout the product life cycle” “models are used to drive all aspects of the product lifecycle and that data is created once and reused by all downstream data consumers.”

• Centric: “is a focus entirely on development of information of and about the system – the model”

• Oriented: “ is a paradigm that does not differentiate between modelling and coding. This is referred to as model-code duality.”

http://onlinelibrary.wiley.com/doi/10.1002/spe.1155/abstract#spe1155-note-0001

http://www.3dcadworld.com/why-you-need-to-understand-model-based-engineering/

https://www.youtube.com/watch?v=VjGmNjg5cro

http://cruise.eecs.uottawa.ca/umple/

Mission Simulation LabHICEEModel (driven x based x centric x oriented)

Model Based

Model Driven

ModelOriented

Model Centric

Document Based“data-source”

“generation”

“focus”

“language”

Mission Simulation LabHICEEModels...

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Engineering?

Modelling andauto-build?

Mission Simulation LabHICEEBenefits

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http://www.eclipse.org/proposals/eclipse-mddi/

Mission Simulation LabHICEESame kernel step each activity of the Lifecycle

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Mission Simulation LabHICEEV-MODEL

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V – WaterfallYs – Prototype Cascade

cscerqueira.com.br

0 A B C D E F

Mission Simulation LabHICEEStill alive?

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CASE: CUBESAT ALFA

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Mission Simulation LabHICEEWhat is Alfa!?• 1st CubeSat Mission of CTEE Program

• CTEE – Capacitação Técnica em Engenharia Espacial

• Mission:• Validate Service Bus / Com Networks• Shielding / Error Recovery Experiments

• Current Status: • Finishing Documents to MDR

• Space Segment• OnBoard Computer• Telecomunications• Power Supply• Attitude (at least determination)• Structure• Experiments

• Ground Segment• Receive telemetries (housekeeping /

mission)• Send telecommands

https://gomspace.com

Mission Simulation LabHICEESimplified Timeline

Separation10min start

LEOP Housekeeping and Determination TM

Manouver& Ex. Release

ExperimentTM

EOLPre-FlightTesting

LaunchOps

Mission Simulation LabHICEEList all possible CubeSat Events

Release Pin

10min LEOP timer

Charging/Antenna Release

Orientation Ok

Experiment Released ok

Receive Reset TC

Failure Occur

Failure Solved

Press Pin

Mission Simulation LabHICEESelect a few events to model

• ON

• OFF

• OK

• FAILURE

Software

• NONE

• SERVICE

• PAYLOAD

• MANOUVER

• EOL

• END_HANDLE

Telecommand

2*3*6 = 36 states

All are possibles?All are feasibles?All should exist?All are achievables?

Mission Simulation LabHICEEStates

Failure

Pre-Lauch

Launch

HandleServ. TC

HandleManouver

HandlePay. TC

Stand-By

Pin=off/cb=off

Pin=on/cb=start

SW=ok/cb=normal

SW=Fail/cb=checkSW=Fail/cb=check

SW=Fail/cb=check

SW=ok/cb=normal

Tc=serv/cb=servHandle

Tc=aocs/cb=manHandle

Tc=EOL/cb=eolHandle

Tc=endHandle|| timeout/cb=norm

Tc= endHandle|| timeout /cb=norm

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Events table:Pre-

FlightTesting

LaunchOps

LEOP Stand-by HandleService

HandlePayload TC

HandleManouver

EOL Failure

Pre-FlightTesting

x Pin = OFF

LaunchOps

x Pin = ON

LEOP x SW = OK SW = Failure

Stand-by x TC = serv

TC = pay TC = man TC = eol

SW = Failure

Handle Service TC

TC = endHandle|| timeout

x SW = Failure

Handle PayloadTC

x SW = Failure

HandleManouver TC

x SW = Failure

Failure SW = OK x

OPM – ObjectProcess

Methodology

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Mission Simulation LabHICEEPhase 0 – Concepts - OPM

• The Start

• What is OPM?!

• How to MEF?!

• Alfa Example

Mission Simulation LabHICEEThe start:

How do we explain ideas to each other?

•Grab a pen and piece of paper, or a chalk and blackboard

•Scribble shapes with names next to them

•While talking, run lines with or without arrows among the shapes

•Follow the reaction of the audience to see if idea is understood

•Answer questions, continue scribbling…

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These “first” ideias Conceptual Modelling

•A systematic, formalized process of describing, specifying, designing or explaining ideas, systems, products or processes through a model

•Applicable to both:• Science – Studying what is known and what is missing to

satisfy human thirst for knowledge, and • Engineering – Designing systems to benefit humans,

based on sound scientific principles

•Science can be thought of as reverse engineering of nature

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Mission Simulation LabHICEEOPM (Object-Process Methodology)

Prof. Dov Dori

Created in 2002

improving and showingit applicability

• A Single Diagram – Maps Behaviour and Structure• 2 Building Blocks and 10 basic relations• Designed to “Systemic View” and “Concept

Design”• Simulation Ready

• ~130 Pages standard• Published in late 2015• Intended to “Automation Systems and

Integration”• Has the “power” of a ISO seal.

Mission Simulation LabHICEE[Dori] Object-Process Theorem

Objects with states, processes andtheir relations among them constitutea necessary and sufficient universal

ontology to describe a system.

Theoretical Foundations

• Dual Channel Processing (visual and verbal)

• Limited Capacity – amount of human processing.

• Active Processing – learning occurs during active cognitive processing. (Simulation)

• Ontology: set of concepts for describing a domain

• Universal Ontology: a domain-independent set of concepts for describing systems.

08:20 38http://esml.iem.technion.ac.il/wp-content/uploads/2015/02/webinar_020915.pdf

• simple yet expressive, and

• intuitive yet formal

The Start:

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Convert

Construct

Communicate

Concept

Models

fragmented knowledge intoexplicit and integratedknowledge

concise models

The model tostakeholders

Mission Simulation LabHICEEOPM’s only two building blocks:

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Product

testing

not tested tested1. Objects with states2. Processes

All the other elements are relations between things, Expressed graphically as links

Mission Simulation LabHICEETwo simultaneous cognitive channels: visual-

OPD and textual-OPL

Diagram (OPD) Textual (OPL)

• Christopher is physical.Christopher knows OPM Theory.Christopher handles presenting.OPM consists of OPM Theory.presenting is physical.presenting consumes OPM Theory.presenting yields Presentation.

Mission Simulation LabHICEEMajors aspects of any system?

• Structure – the static aspect. What the system is made of. • Time-independent

• Behavior – the dynamic aspect. How the system changes over time.• Time-dependent

• Function – The utilitarian, subjective aspect.• Why is the system built?

• For whom is the system built?

• Who benefits from operating the system?

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Mission Simulation LabHICEEStructural Links

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StructuralRelational

Aggregation

Exhibition

Specialization

Instantiation

Mission Simulation LabHICEEBehaviour (Procedural Links)

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Procedural

Transforming

• Consume

• Create

• Effect

Enabling

• Agent

• Instrument

Control

• Events

• Conditions

• Invocation

• Exceptions

Mission Simulation LabHICEEOther Mechanisms

Operators• Multiplicity

• Logical

Complexity

• Zoom-In – Zoom-Out

• Unfolding - Folding

• State expression –suppression

Simulation

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“Models interrelation”

Mission Simulation LabHICEEExamples:

Object-Process Methodology and Its Application to the Visual Semantic Web

Mission Simulation LabHICEEBrief comparison: SysML and OPM

Feature SysML OPM

Theoretical Foundation UML; Object-Oriented Paradigm

Minimal Universal Ontology;Object-Process Theorem

Standard Documentationnumber of pages

1670 130

Standardization body OMG (2006) ISO (2015)

Number of diagram kinds 9 1

Graphic modality Yes Yes

Textual modality No Yes

Physical-Informatical Distinction No Yes

Systemic-Environmental Distinction

No Yes

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Mission Simulation LabHICEEModelling Alfa’s State Behaviour

Mission Simulation LabHICEESimpler Example – Logics

p1 changes a from s1 to s2.

Following path aa, p3 consumes s1 a.Following path bb, p3 consumes s2 a.p3 yields s3 a.

Mission Simulation LabHICEESimpler Example – states of the water

Mission Simulation LabHICEEModelling Alfa’s State Behaviour

Mission Simulation LabHICEEOPM State Charts 1/6

08:20cscerqueira.com.br 52https://www.research.ibm.com/haifa/info/ple/present/Dori-States-OPM-Statecharts.pdf

Mission Simulation LabHICEEFormally

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•OPM just turned to be a ISO standard. (15-Dec-2015)

•Already being looked by airspace companies ( EMBRAER / BOEING )

•Already being researched to Concurrent Engineering(Cambridge / INPE)• Knowledge Based Systems

•Simpler to implement than any UML/SysML specification.• Has direct compatibility with SysML models (back/forth) • Exports to Matlab (AUTOMATLAB)

•Dual Channel textual + visual • Extra third Channel simulation

• Personal touch:• More understandable than any UML-like to non-computer

specialists.End of Class 01

ARCADIA (SysML-Like)

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Mission Simulation LabHICEEPhase 0/A – Concepts/Architecture - Arcadia

• What is?!

• How to MEF?!

• Alfa Example

Mission Simulation LabHICEEHistory of Object Oriented Modeling languages

08:20cscerqueira.com.br 61SysML 1.4

Mission Simulation LabHICEEUML – Unified Modelling Language

Mission Simulation LabHICEEAdaptation to Systemic Domain

Mission Simulation LabHICEESysML – System Modelling Language

Mission Simulation LabHICEESysML Diagram Branches

Mission Simulation LabHICEESysML

•Is a visual modelling language that provides•Semantics = meaning

•Notation = representation of meaning

•Is not•a methodology or a tool•SysML is methodology and tool independent

08:20cscerqueira.com.br 66http://www.omgsysml.org/INCOSE-OMGSysML-Tutorial-Final-090901.pdf

Mission Simulation LabHICEEPolarSys Proposal for MBSE

Mission Simulation LabHICEEArcadia / Capella

• Polarsys Group• Thales Alenia

• Phases 0 - C

• Capella is a layer above the Eclipse IDE, created using the Sirius and the Eclipse Modelling Framework (EMF)

• Diagrams: • operational

architecture,

• use cases,

• dataflow,

• architecture,

• logical,

• physical,

• tree,

• sequence,

• state,

• and classes 8:20 68https://www.polarsys.org/capella/

Mission Simulation LabHICEESystems Architecture Modeling with the Arcadia Method

A Practical Guide to Capella

ARCADIA - ARChitecture

Analysis and Design

Integrated Approach

Mission Simulation LabHICEEDiagram Examples

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Mission Simulation LabHICEEMethodological Guidance

Define Stakeholder Needs and EnvironmentCapture and consolidate operational needs from stakeholders. Define what the users of the system have to accomplish Identify entities, actors, roles, activities, concepts

Formalize System RequirementsIdentify the boundary of the system, consolidate requirements. Define what the system has to accomplish for the users. Model functional dataflows and dynamic behaviour

Develop System Logical ArchitectureSee the system as a white box: define how the system will work so as to fulfill expectations Perform a first trade-off analysis

Develop System Physical ArchitectureHow the system will be developed and built Software vs. hardware allocation, specification of interfaces, deployment configurations, trade-off analysis

Formalize Component RequirementsManage industrial criteria and integration strategy: what is expected from each designer/sub-contractor. Specify requirements and interfaces of all configuration items

Mission Simulation LabHICEESpiral

ActorsEntitiesNeeds

Activities

Scenarios

MissionsCapabilities

Functions

ArchitectureFunctions

Components

Functions

Components

Operational Analysis

System Analysis

Logical Architecture

Physical Architecture

Modes & States

Architecture

(Christopher)

Classes

Mission Simulation LabHICEESpiral

Operational Analysis

System Analysis

Logical Architecture

Physical Architecture

(Christopher)

Classes

[OEBD][OCB]

[OAIB] [OAS]

[SDFB]

[LFBD]

[LDFB]

[LCBD][LAB]

[PFBD]

[PDFB]

[PCBD][PAB]

Clock Radio Example

OperationalOAB - Entities and Activities

OEBD - Entities

System

SABs – Components, Functions and Exchanges

SDFBs – Functions and Exchanges

SM&S – Modes and States

SES – Entities Exchange Scenarios

SFC – Functional Chains

Logical

LABs – Components, Functions and Exchanges

LDFB – Data flows (functions and exchanges)

LFBD – Function Breakdown

LES – Exchange Scenarios

LFCD – Functional Chains...

Define Stakeholder Needs and EnvironmentCapture and consolidate operational needs from stakeholders. Define what the users of the system have to accomplish Identify entities, actors, roles, activities, concepts

Create Stkse needs

CreateActivities

CreateStates/Modes

DescribeEvents

Failure

Pre-Lauch

Launch

HandleServ. TC

HandleManouver

HandlePay. TC

Stand-By

Pin=off/cb=off

Pin=on/cb=start

SW=ok/cb=normal

SW=Fail/cb=check

SW=ok/cb=normal

Tc=EOL/cb=eolHandle

Formalize System RequirementsIdentify the boundary of the system, consolidate requirements. Define what the system has to accomplish for the users. Model functional dataflows and dynamic behaviour

Import fromOp. Analysis

Create the System and Allocate

functions

CreateStates/Modes

DescribeEvents

Mission Simulation LabHICEECapella Ecosystem

In Software Code

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Mission Simulation LabHICEEPhase C – Code

• What is?!

• How to MEF?!

• Alfa Example

• Beyond...

Mission Simulation LabHICEEModel a Behaviour

http://gameprogrammingpatterns.com/state.html

Mission Simulation LabHICEEEnum and

Switch approach

enum State { STATE_STANDING, STATE_JUMPING, STATE_DUCKING, STATE_DIVING };

void Heroine::handleInput(Input input) {

switch (state_) {

case STATE_STANDING:

if (input == PRESS_B) { state_ = STATE_JUMPING;

yVelocity_ = JUMP_VELOCITY; setGraphics(IMAGE_JUMP);

} else if (input == PRESS_DOWN) {

state_ = STATE_DUCKING; setGraphics(IMAGE_DUCK);

} break;

case STATE_JUMPING:

if (input == PRESS_DOWN) {

state_ = STATE_DIVING; setGraphics(IMAGE_DIVE);

} break;

case STATE_DUCKING:

if (input == RELEASE_DOWN) {

state_ = STATE_STANDING; setGraphics(IMAGE_STAND);

} break;

Mission Simulation LabHICEEObjects

class Heroine {

public:

virtual void handleInput(Input input);

virtual void update();

// Other methods...

private:

HeroineState* state_;

Mission Simulation LabHICEEState Design Pattern

class Class Model

Heroine

+ handleInput(Input) :void

HeroineState

+ update() :void

+ handleInput(Heroin&, Input) :HeroineState*

Ducking

+ update() :void

Standing

+ update() :void

Jumping

+ update() :void

Div ing

+ update() :void

Mission Simulation LabHICEETo control multiple things...

Concurrent StateCharts

08:20cscerqueira.com.br 88http://www.ni.com/tutorial/6194/en/

Mission Simulation LabHICEEStates and Concurrent FSMs

Abstract SW State

Abstract Stateof something

HandServ

StandBy

HandPay

HandMan

HandEOL

Failure

Concurrent FSMs

Mission Simulation LabHICEEBioSystems / Nano / Additive / IoT

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Mission Simulation LabHICEENew motto

CTEE 9208:20

MATLAB / Simulink

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Mission Simulation LabHICEEPhase B/C – Digital Design – Matlab/Simulink

• What is?!

• What is possible to model?!

• How to MEF?!

• Alfa Example

Mission Simulation LabHICEESimulink

• Modeling/designing dynamic systems (including nonlinear dynamics)

• Modeling/designing control systems (including nonlinear controllers and plants)

• Signal processing design/simulation

• Simulink runs under Matlab. First start Matlab, then type “simulink” at the Matlab prompt.

08:20cscerqueira.com.br 95http://emerald.tufts.edu/~rwhite07/PRESENTATIONS_REPORTS/simulink.pdfhttps://pt.slideshare.net/christophercerqueira/prj32christopher-aula-14-controle

Mission Simulation LabHICEEExample

• Ramp with control

• Wave with filter

• Subsystem all

Mission Simulation LabHICEEState flow

• sf_seqrec

Mission Simulation LabHICEESignal Source

Mission Simulation LabHICEEMoore

1121211213

Mission Simulation LabHICEEMealy

1121211213

Mission Simulation LabHICEEComparing

1121211213

Failure

Pre-Lauch

Launch

HandleServ. TC

HandleManouver

HandlePay. TC

Stand-By

Pin=off/cb=off

Pin=on/cb=start

SW=ok/cb=normal

SW=Fail/cb=check

SW=ok/cb=normal

Tc=EOL/cb=eolHandle

Mission Simulation LabHICEEWhich is done inside the SUD

HandServ

HandPay

HandMan

HandEOL

Failure

InitPin

TCsTMs

StandBy

Mission Simulation LabHICEEAlfa’s FSM in Matlab

Mission Simulation LabHICEESimulating

ARDUINO

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Mission Simulation LabHICEEPhase C – Automatic Realization

• What is?!

• What is possible to model?!

• How to MEF?!

• Alfa Example

Mission Simulation LabHICEEARDUINO

08:20cscerqueira.com.br

Mission Simulation LabHICEEShields

08:20cscerqueira.com.brhttp://www.telecom.uff.br/pet/petws/downloads/tutoriais/arduino/Tut_Arduino.pdf

Mission Simulation LabHICEETesting

• Generate pulse

• Stop blinking via INPUT state pin

Mission Simulation LabHICEEExposing interfaces (stimuli/response) HW

TCsTMs

D2 – Pin

D3 – TC01D4 – TC02D5 – TC03D6 – TC04D7 – TC05D8 – TC06D9 – TC07

Blink pattern pin 13State -1 – failure : fastState 0 – init : offState 1 – stand : onState 2 – serv : oneState 3 – pay : twoState 4 – man : threeState 5 – eol : four

Mission Simulation LabHICEEExposing interfaces

Mission Simulation LabHICEEMicroprocessor x Microcontroller x Application

Processor

“only process”

dedicated task

generic

“Complete sollution toindustry”

Complete computationalarchitecture (memory, IOs)

Industry and automation

“Complete sollutionto enduser”

Complete computationalarchitecture(memory, IOs)

End user apps

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Using Finite State Machines From Concept to Realization

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