Digital Transformation#1: MATLAB e Simulink per supportare Modellazione & Simulazione dei sistemi in Industry 4.0 - Aldo Caraceto Application ...
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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
© 2020 The MathWorks, Inc.
1
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
2
Digital Transformation Industriale: opportunità, sfide e
soluzioni per lo sviluppo prodotto
4
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”
5
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
6
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
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
Approaches and Enablers
to address these Challenges
9
Key Enabler: Mechatronics
Combination of mechanical-, computer-,
telecommunications-, systems- and control
engineering with electronics
10
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
11Key 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
12MATLAB e Simulink per
Modellazione & Simulazione di sistemi virtuali
in Industry 4.0
13Modellazione e Simulazione in Industry 4.0
Industry 4.0 Levers
▪ «Rapid Experimentation and
Value Drivers
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 14Modellazione e Simulazione in Industry 4.0
15Modellazione e Simulazione in Industry 4.0 “Product Life Cycle Risk Management”, Jan Machac, Frantisek Steiner and Jiri Tupa, 2017 16
Model-Based Design
RESEARCH REQUIREMENTS
What if you were able to verify your system’s
behavior through the entire design process?
DESIGN
Environment Models
TEST & VERIFICATION
Physical Plant Models
Control / Supervisory Logic Models
IMPLEMENTATION
C, C++ IEC HDL
MCU DSP PLC FPGA ASIC
INTEGRATION / COMMISSIONING
17Model-Based Design
RESEARCH REQUIREMENTS Step 1: Desktop Simulation
DESIGN
▪ Prototype new functionality and
Environment Models combine with existing code
TEST & VERIFICATION
Physical Plant Models ▪ Perform automated system tests
Control / Supervisory Logic Models
that would not be feasible outside of
simulation
▪ Optimize parameters (software,
IMPLEMENTATION
mechanics, hydraulics, etc.)
C, C++ IEC HDL
MCU DSP PLC FPGA ASIC
INTEGRATION / COMMISSIONING
18Model-Based Design
RESEARCH REQUIREMENTS Step 2: Hardware in the Loop
DESIGN
▪ Emulate the behavior of the physical
Environment Models system in real-time
TEST & VERIFICATION
Physical Plant Models ▪ Connect the virtual plant to your
Control / Supervisory Logic Models
PLC or industrial PC
IMPLEMENTATION
C, C++ IEC HDL
MCU DSP PLC FPGA ASIC
INTEGRATION / COMMISSIONING
19Model-Based Design
RESEARCH REQUIREMENTS Step 3: Production Use
DESIGN
▪ Design and test hardware
Environment Models independent functionality
TEST & VERIFICATION
Physical Plant Models
Control / Supervisory Logic Models
IMPLEMENTATION
C, C++ IEC HDL
MCU DSP PLC FPGA ASIC
INTEGRATION / COMMISSIONING
20Simulink to support Model-Based Design
21Esempi di sviluppo di Sistemi di Controllo
per Apparati Meccatronici
22Modeling Physical Systems with MathWorks Products
Modeling Approaches
First Principles Modeling Data-Driven Modeling
Physical Networks Statistical Methods
Programming (Statistics & Machine System
(MATLAB, C) (Simscape Products)
Learning Toolbx) Identification
(System Identification
Block Diagram Toolbox)
(Simulink)
Modeling Language Neural Networks
(Simscape language) (Deep Learning
Toolbox)
Symbolic Methods Parameter Tuning
(Symbolic Math (Simulink Design Optimization)
Toolbox)
23Modellazione di Sistemi a partire da Misurazioni sul Campo
24Modeling Approaches
First Principles Data-Driven
Physical Networks Statistical Methods
Programming System
Identification
Measured
Block Diagram
Model
Modeling Language Neural Networks
Symbolic Methods
▪ Purpose: Model an existing design (real or virtual)
▪ Requirements:
– Relevant set of measured data is available
– Design and physical parameters will not be changed
25Modeling Approaches: System Identification
System
Measured input + error
-
Model Minimize
26Estimation 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.
Error
Validation data
Estimation data
Number of parameters
27Example: Indentification of a Linear System
28Using 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
30Using an Estimated Model in Simulink
▪ Use models estimated in System
Identification Toolbox directly in a
Simulink model
▪ Blocks available for source, sink
and models
31Modellazione di Sistemi Multifisici
32Physical Modeling
First Principles Data-Driven
Physical Networks
Programming
Block Diagram
Modeling Language
Symbolic Methods
▪ Purpose: Explore design or physical parameters
▪ Requirements:
– Physics of system are well-known
– Component-level models exist or can be created
33Motivation
Controller Plant
Controller Plant Controller
Plant 34Optimize System-Level Performance
Actuators
Sensors
u + s1 s2
System
y
s3
Controller Plant
Simulating plant and controller in one environment
allows you to optimize system-level performance
– Automate tuning using optimization algorithms
– Accelerate process using parallel computing
35Detect Integration Issues Earlier
Actuators
Sensors
+
uSystem
s1 s2
System
y
s3 System
Specification Model
Controller Plant
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
36Build Accurate Models Quickly
▪ Simply connect the FSpring = k Spring *(xMass )
components you need dxMass
FDamper = bDamper *( )
dt
d2 xMass −FSpring − FDamper
▪ The more complex the =
dt 2 mMass
system, the more value
you get from Simscape
▪ Resulting model is
intuitive, easy to modify,
and easy for others Input/Output Block Diagram Simscape
to understand
37Build Accurate Models Quickly
Fortran, C++
Domain Expertise Coding Effort
System MATLAB, Simulink System
Specification Domain Expertise Coding Effort Model
Simscape
Domain Exp. Coding Eff.
Get from specification to model even faster
Spend more time designing, less time modeling
38Example: Robot Arm and Conveyer Belts
39Example: Modeling Contact Force Between Two Solids
40Example: Modeling a Three-Phase Inverter
41Simscape Products
▪ Simscape platform
– Foundation libraries in many domains
– Language for defining custom blocks
Isothermal
▪ Extension of MATLAB Liquid
– 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
42Optimize Your Entire Engineering System
Multidomain Simscape Domain Exp. Coding Eff. Plant
Model
Multibody Domain Exp. Coding Eff.
Mechanical
Driveline Domain Exp. Coding Eff.
Hydraulic
Fluids Domain Exp. Coding Eff.
Electronic
Power Systems Electrical Domain Exp. Coding Eff.
Simulate the entire system in a single environment
– Does not require learning multiple tools or co-simulation
43Simscape 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
44Modellazione di Sistemi a partire da Equazioni
45Equation–based Modeling
First Principles Data-Driven
Programming
Block Diagram
Modeling Language
Symbolic Methods
▪ Purpose: Explore design or physical parameters
▪ Requirements:
– Physics of system are well-known
– System-level equations can be derived and implemented
46Customize and Extend Simscape Libraries for a Custom DC Motor
47Dal Disegno Meccanico alla Regolazione dell’unità di
Motion Control per un sistema Meccatronico
48Optimizing 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
49Progettazione del sistema di regolazione del tiro per film plastici
Closed-loop model
Outputs
Control logic
50What is Stateflow?
Extend Simulink with
state charts and flow
graphs
Design supervisory
control, scheduling, and
mode logic
Model state
discontinuities and
instantaneous events
51How Does Stateflow Work with Simulink?
Simulink excels at Stateflow excels at
continuous changes in instantaneous changes in
dynamic systems. dynamic systems.
Real-world systems have to respond to both continuous and
instantaneous changes.
suspension dynamics
gear changes manufacturing robot
propulsion system operation modes
liftoff stages
Use both Simulink and Stateflow so that you
can use the right tool for the right job. 52Key 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
53MATLAB e Simulink per supportare
Modellazione & Simulazione dei sistemi in Industry 4.0
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