digital transformation#1: matlab e simulink per supportare ......5 digital transformation: learnings...

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Digital Transformation#1:

MATLAB e Simulink per supportare

Modellazione & Simulazione dei sistemi in Industry 4.0

Aldo Caraceto

Application Engineering Group

2

Modellazione & Simulazione dei sistemi in Industry 4.0 - Agenda

Orario Titolo della Sessione

09:00 Digital Transformation Industriale: opportunità, sfide e soluzioni per lo sviluppo prodotto

09:10 MATLAB & Simulink per Modellazione & Simulazione di sistemi virtuali in Industry 4.0

09:20 Esempi di sviluppo di sistemi di controllo per apparati meccatronici

• Modellazione di plant a partire da equazioni/multifisici

• Progettazione di controlli continui e a logiche ad eventi discreti

10:05 Q&A

4

Digital Transformation Industriale: opportunità, sfide e

soluzioni per lo sviluppo prodotto

5

Digital Transformation: Learnings from studies and programs

▪ Customers want increasingly individualized products. “sample-size 1”

▪ Autonomous machines which do not require costly programming to meet

new requirements. “Smart products”

▪ Intelligent products that collect data to optimize processes and develop new products

▪ Competitive threats from big players offering internet-related and IT products and

services.

▪ Opportunities for innovative business models and services. Particularly for SME’s.

“Servitization”

6

Digital Transformation of the Industry: is everywhere

– Higher flexibility given by small batches production

with the economies of scale

– Higher speed from prototyping to mass production

using innovative technologies

– Increased productivity thanks to lower set-up time

and reduced downtimes

– Improved quality and scrap reduction thanks to

real time production monitoring through sensors

– Higher competitiveness of products thanks to

additional functionalities enabled by Internet Of

Things

7

The birth of New Challenges designing multi-domain, smart, connected

systems

▪ Too slow because process is serial and fragmented, many iterations are needed

▪ Components over- under dimensioned

▪ System Performance issues detected too late in integration phase

▪ Need risky/expensive physical machine testing

▪ Tuning and commissioning is lengthy

▪ Need to design more intelligent and connected systems

▪ Need customizable systems without extensive re-design and programming

8

The birth of New Challenges designing multi-domain, smart, connected

systems

▪ Too slow because process is serial and fragmented, many iterations are needed

▪ Components over- under dimensioned

▪ System Performance issues detected too late in integration phase

▪ Need risky/expensive physical machine testing

▪ Tuning and commissioning is lengthy

▪ Need to design more intelligent and connected systems

▪ Need customizable systems without extensive re-design and programming

9

Approaches and Enablers

to address these Challenges

10

Key Enabler: Mechatronics

Combination of mechanical-, computer-,

telecommunications-, systems- and control

engineering with electronics

11

Key Enabler: Cyber Physical System

▪ A mechanism controlled by

computer-based algorithms,

tightly integrated with the Internet

▪ Process control based on

embedded systems

▪ Examples: smart grid, autonomous

automobile, medical monitoring, robotics

12

Key Enabler: Digital Twin

▪ A digital replica of physical assets,

that can be used for various purposes.

▪ Integrate machine learning and analytics

to create living digital simulation models

that continuously learn and update

themselves

13

MATLAB e Simulink per

Modellazione & Simulazione di sistemi virtuali

in Industry 4.0

14

Modellazione e Simulazione in Industry 4.0

▪ «Rapid Experimentation and

Simulation»

è una leva fondamentale per

ridurre Time-to-Market

▪ La simulazione è solo un

elemento; altri devono essere

resi disponibili per il massimo

risultato possibile.

▪ Molteplici driver determinano il

successo di un progetto: es.

«Time-to-Market», senza

«Quality»?

“Industry 4.0. How to navigate digitalization of the manufacturing sector” – McKinsey Digital, 2016

Industry 4.0 Levers

Value Drivers

15


16


“Product Life Cycle Risk Management”, Jan Machac, Frantisek Steiner and Jiri Tupa, 2017

17

Model-Based Design

INTEGRATION / COMMISSIONING

IMPLEMENTATION

PLCMCU DSP FPGA ASIC

IEC HDLC, C++

DESIGN

Environment Models

Physical Plant Models

Control / Supervisory Logic Models

TE

ST

& V

ER

IFIC

AT

ION

RESEARCH REQUIREMENTSWhat if you were able to verify your system’s

behavior through the entire design process?

18

Model-Based Design


IMPLEMENTATION


IEC HDLC, C++

Step 1: Desktop Simulation

▪ Prototype new functionality and

combine with existing code

▪ Perform automated system tests

that would not be feasible outside of

simulation

▪ Optimize parameters (software,

mechanics, hydraulics, etc.)

DESIGN

Environment Models



RESEARCH REQUIREMENTS

TE

ST

& V

ER

IFIC

AT

ION

19

Model-Based Design

Step 2: Hardware in the Loop

▪ Emulate the behavior of the physical

system in real-time

▪ Connect the virtual plant to your

PLC or industrial PC


TE

ST

& V

ER

IFIC

AT

ION

IMPLEMENTATION


IEC HDLC, C++

DESIGN

Environment Models




20

Model-Based Design

Step 3: Production Use

▪ Design and test hardware

independent functionality


TE

ST

& V

ER

IFIC

AT

ION

IMPLEMENTATION


IEC HDLC, C++

DESIGN

Environment Models




21

Simulink to support Model-Based Design

22

Esempi di sviluppo di Sistemi di Controllo

per Apparati Meccatronici

23

Data-Driven ModelingFirst Principles Modeling

Neural Networks

Physical NetworksSystem

Identification

Parameter Tuning

Programming

Block Diagram

Modeling Language

Symbolic Methods

Modeling Approaches

Modeling Physical Systems with MathWorks Products

Statistical Methods

(MATLAB, C)

(Simulink)

(Simscape language)

(Symbolic MathToolbox)

(Simscape Products)

(Deep Learning Toolbox)

(Statistics & Machine Learning Toolbx)

(Simulink Design Optimization)

(System Identification Toolbox)

24

Modellazione di Sistemi a partire da Misurazioni sul Campo

25

Modeling Approaches

▪ Purpose: Model an existing design (real or virtual)

▪ Requirements:

– Relevant set of measured data is available

– Design and physical parameters will not be changed

Data-DrivenFirst Principles

Physical NetworksProgramming

Block Diagram

Modeling Language

Symbolic Methods

Neural Networks

System Identification

Statistical Methods

MeasuredModel

26

Modeling Approaches: System Identification

System

Model

+

-Minimize

errorMeasured input

27

Estimation and Validation Go Together

▪ A large enough model can reproduce a measured output arbitrarily well. We

must verify that model is relevant for other data – data that was not used for

estimation, but was collected for the same system.

Err

orNumber of parameters

Estimation data

Validation data

28

Example: Indentification of a Linear System

30

Using System Identification Toolbox

▪ Use two data sets for estimation

and validation

▪ Estimate a variety of models:

▪ Linear models – Transfer

functions, state space, etc.

▪ Nonlinear models – ARX-

type and Hammerstein-

Wiener

▪ Nonparametric – Impulse

and frequency response

▪ Grey-Box models – Models

with known structure

but unknown

parameters

31

Using an Estimated Model in Simulink

▪ Use models estimated in System

Identification Toolbox directly in a

Simulink model

▪ Blocks available for source, sink

and models

32

Modellazione di Sistemi Multifisici

33

Physical Modeling

▪ Purpose: Explore design or physical parameters

▪ Requirements:

– Physics of system are well-known

– Component-level models exist or can be created


Physical NetworksProgramming

Block Diagram

Modeling Language

Symbolic Methods

34

PlantController

ControllerPlant

Controller Plant

Motivation

35

Simulating plant and controller in one environment

allows you to optimize system-level performance– Automate tuning using optimization algorithms

– Accelerate process using parallel computing

Optimize System-Level Performance

Plant

+u y

Controller

s1 s2

s3

System

Actu

ato

rs

Sen

so

rs

36

System

Model

System

Specification

Detect Integration Issues Earlier

Plant

+u y

Controller

s1 s2

s3

Controls engineers and domain specialists can work together to detect integration issues in simulation

– Convert models to C code for HIL tests

– Share with internal users with fewer licenses

– Share with external users while protecting IP

System

Actu

ato

rs

Sen

so

rs

37

Build Accurate Models Quickly

▪ Simply connect the

components you need

▪ The more complex the

system, the more value

you get from Simscape

▪ Resulting model is

intuitive, easy to modify,

and easy for others

to understand

FSpring = kSpring*(xMass)

FDamper = bDamper*(dxMass

dt)

d2xMass

dt2=−FSpring − FDamper

mMass

Input/Output Block Diagram Simscape

38

Build Accurate Models Quickly

Get from specification to model even fasterSpend more time designing, less time modeling

Simscape

MATLAB, Simulink

Domain Expertise Coding Effort

Coding Eff.Domain Exp.

System

Specification

Fortran, C++

Domain Expertise Coding Effort

System

Model

39

Example: Robot Arm and Conveyer Belts

40

Example: Modeling Contact Force Between Two Solids

41

Example: Modeling a Three-Phase Inverter

42

Simscape Products

▪ Simscape platform

– Foundation libraries in many domains

– Language for defining custom blocks

▪ Extension of MATLAB

– Simulation engine and custom diagnostics

▪ Simscape add-on libraries

– Extend foundation domains with

components, effects, parameterizations

– Multibody simulation

– Editing Mode permits use of add-ons

with Simscape license only

– Models can be converted to C code

Isothermal

Liquid

43

Optimize Your Entire Engineering System

Mechanical

Electronic

Multidomain

Hydraulic

Simulate the entire system in a single environment– Does not require learning multiple tools or co-simulation

Coding Eff.

Power Systems

Simscape Domain Exp.

Multibody Coding Eff.Domain Exp.

Driveline Coding Eff.Domain Exp.

Fluids Coding Eff.Domain Exp.

Electrical Coding Eff.Domain Exp.

Plant

Model

44

Simscape Add-on Libraries

▪ Simscape Electrical

– Electronics, mechatronics, and power systems

▪ Simscape Driveline

– Gears, leadscrew, clutches, tires, engines

▪ Simscape Multibody

– Multibody systems: joints, bodies, frames

▪ Simscape Fluids

– Pumps, actuators, pipelines, valves, tanks

45

Modellazione di Sistemi a partire da Equazioni

46

▪ Purpose: Explore design or physical parameters

▪ Requirements:

– Physics of system are well-known

– System-level equations can be derived and implemented


Programming

Block Diagram

Modeling Language

Symbolic Methods

Equation–based Modeling

47

Customize and Extend Simscape Libraries for a Custom DC Motor

48

Dal Disegno Meccanico alla Regolazione dell’unità di

Motion Control per un sistema Meccatronico

49

Optimizing Time-Domain Responses of a Simulink Model

▪ Specify desired behavior by either graphically shaping the

desired response or typing in numeric values

▪ Add design requirements without adding blocks to the

model

▪ Use multiple objectives and constraints simultaneously

▪ Monitor all plots in one window

▪ Perform optimization faster with Parallel Computing

Toolbox and Fast Restart

50

Progettazione del sistema di regolazione del tiro per film plastici

Closed-loop model

Control logic

Outputs

51

What is Stateflow?

Extend Simulink with state charts and flow graphs

Design supervisory control, scheduling, and mode logic

Model state discontinuities and instantaneous events

52

How Does Stateflow Work with Simulink?

Simulink excels at continuous changes in dynamic systems.

Stateflow excels at instantaneous changes in dynamic systems.

Real-world systems have to respond to both continuous and

instantaneous changes.

suspension dynamics

gear changespropulsion system

liftoff stages

manufacturing robot

operation modes

Use both Simulink and Stateflow so that you can use the right tool for the right job.

53

Key Takeaways

▪ MATLAB e Simulink forniscono un ambiente integrato per sviluppare

progetti innovativi all’interno del paradigma di Industry 4.0

▪ MATLAB e Simulink supportano efficacemente la modellazione &

simulazione di sistemi complessi, fornendo:

1. strumenti per intercettare eventuali errori nelle fasi preliminari

2. funzionalità per limitarne l’introduzione accidentale.

▪ MATLAB e Simulink garantiscono un supporto completo e un flusso di

lavoro ininterrotto all’interno del Model-Based Design

54

MATLAB e Simulink per supportare

Modellazione & Simulazione dei sistemi in Industry 4.0

digital transformation#1: matlab e simulink per supportare ......5 digital transformation: learnings...

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