Index

Note: ‘Page numbers followed by “f” indicate figures and “t” indicate tables’.

A
Amazon Cloud Services, See Amazon Web Services (AWS)
Amazon in cloud platform services, 17
Amazon S3 Bucket, 283–334
Amazon Web Services (AWS), 203 , 339
certificates, 253f , 259f
cloud, 9 , 513
connectivity to real-time embedded hardware for DC motor speed control, 397–458
DC motor control digital twin deployment overall block diagram, 405f
DC motor hardware connectivity and real-time data transfer, 438–458
ESP32 and AWS IoT connection, 442f
ESP32 Arduino software for communication between Arduino and ESP32, 426–438
hardware setup to communicating between Arduino and ESP32, 399–401
new policy, 447f
Simulink model updates for communication between Arduino and ESP32, 411–426
software setup for ESP32 programming, 404–411
wiring and connection table, 406t
console login page, 238f
console login page, 443f–444f
DC motor hardware communication to, 543–545
ESP32 connecting to Wi-Fi HotSpot and, 460f
IoT Core, 233–260 , 240f
services from AWS Console, 445f
IoT Python SDK, 251f
IoT SDK, 250f
IoT Things, 241f , 245f , 263f
attaching policy and registering, 452f
certificate creation window, 451f
certificates generated for, 452f
interact options, 453f
JSON data structure, 459f
loop function, 458f
main page, 446f , 450f
IoT–specific configuration for secure connection, 255f
functions, 283–334
services, 264
SimscapeTM digital twin model deployment to, 545–569
storing Wi-Fi and AWS IoT details in program, 457f
unzip AWS IoT SDK, 251f
Ansys, 5
AnyLogic, 5
Application programming interfaces (APIs), 16
Arduino Mega microcontroller, 339
Artificial intelligence, 16–17
Asset management, 14
Automotive industry, 14
B
Ball on plate, 71
block diagram, 72–73 , 72f
elements, 76f
failure modes and diagnostics concept for, 73
hardware, 71–72 , 72f
interaction, 95–100
ball distance from center line of plate, 97f
pz output from 6-DOF joint, 98f
slipping diagram for x-direction, 100f
slipping diagram for y-direction, 100f
Off-BD
diagnostic process, 73f
steps, 71f
S-function for ball plate interaction, 101–106
Simscape model for, 74–90
simulation of model, 106–110
application problems, 107–108 , 110
Biomedical pump, 15–16 , 16f
C
Charity, 15
Chatbots, 17
Cloud, See Cloud computing
Cloud computing, 11 , 19
5G accelerating, 20
connecting machines to, 13
cost optimization, 18
examples, 20
history, 11–12
platform, 11 , 12f , 16
services, 18
GDPR, 18
security, 18
shadow IT services, 18
simulations, 1
technologies, 1 , 11
applications, 13–18
evolution, 12–13
Cognitive services, 16–17
COMSOL, 5
D
Data monitoring and logging, 221 , 221f
Data science, 17
DC motor control embedded system, 339 , 340f
application problem, 502–512
AWS cloud connectivity to real-time embedded hardware, 397–458
deploying Simscape™ digital twin model to AWS cloud, 480–499
adding trigger for Lambda function from AWS IoT, 502f
AWS IoT trigger added, 503f
browse and select Lambda function Zip File package, 508f
confirmed subscriptions, 487f
creating Email subscription, 485f
creating Text/SMS subscription, 484f
default Lambda function body, 491f
digital twin deployment high level diagram, 480f
importing packages, 504f
Lambda function inline editor, 509f
launching AWS Lambda functions, 488f
Linux command line, 507f
new execution role in Lambda function, 499f
Off-BD algorithm, 506f
pending subscription, 485f
reading input and output files, 505f
reading trigger input data, 504f
root mean squared error, 505f
running digital twin model, 504f
save Lambda function with updated execution role, 500f
saving uploaded Lambda function Package, 508f
SNSs, 481f
subscription confirmation, 486f
uploading packaged Lambda function to AWS, 507f
Off-BD
diagnostics process, 340f
steps, 340f
off-board diagnostics/prognostics algorithm development, 458–479
open-loop data collection and closed-loop PID controller development, 349–366
DC motor nonlinearity analysis using collected data, 359–362 , 361f
running DC motor in open-loop steady-state points, 350–359
steady-state operating points, 361t
open-loop feedforward and closed-loop PID controller design and deployment, 363–366
Arduino PWM Command Subsystem top view, 370f
commanding PWM to PIN 5, 370f
feedforward controller motor speed tracking, 370f
feedforward controller PWM command, 371f
Motor Feedforward + Feedback PID Controller Subsystem, 373f–374f
motor feedforward controller logic, 369f
motor speed reference for MPC controller, 365f
motor speed sensing logic, 367f
motor speed sensing subsystem top view, 366f
reference generation using Simulink Repeating sequence block, 365f
reference generator subsystem top view, 365f
top-level Simulink model of real time motor speed controller, 364f , 372f
parameter tuning of SimscapeTM DC Motor Model, 382–397
comparing simulation and test data with default model parameter values, 402f
extracted linear region 1 data for model parameter estimation, 395f
launching parameter estimation tool, 396f
launching parameter S1election GUI, 399f
new parameter tuning experiment input/output selection window, 398f
optimization progress report, 404f
parameter estimation tool GUI, 397f
parameter values after optimization, 405f
select all model parameters for estimation, 400f
selecting parameters for estimation, 400f
tunable parameters, 401f
real-time embedded controller hardware and software, 341
add-on explorer GUI, 344f
add-on explorer with install button, 346f
Arduino I/O library, 349f
connecting Arduino to DC motor, 343f
DC Motor Controller with Arduino Mega, 341f
hardware requirements and familiarization, 342 , 342t
hardware setup and connection block diagram, 343f
link to example projects available in Support Package, 348f
MathWorks Account login, 347f
Simulink support package for Arduino hardware, 345f
software requirements, 342–349
Support Package installation progress, 347f
Simscape™ digital twin model for DC motor, 368–375
adding DC motor block, 385f
adding H-bridge PWM driver block, 381f
adding ideal rotational motion sensor to DC motor, 389f
adding solver configuration, mechanical, and electrical reference blocks, 393f
completed Simscape™ plant model for DC motor, 394f
controlled PWM voltage and H-Bridge block, 377f
controlled PWM voltage block input scaling setting, 380f
controlled PWM voltage block output voltage setting, 380f
controlled PWM voltage block PWM setting, 379f
DC motor block by Simscape™, 384f
DC Motor Simscape™ plant model configuration settings, 376f
electrical reference block by Simscape™, 391f
H-bridge driver block bridge parameters settings, 383f
H-bridge driver block input threshold setting, 383f
H-bridge driver block simulation mode and load settings, 382f
ideal rotational motion sensor block, 388f
mechanical rotational reference block by Simscape™, 392f
physical input property for PWM voltage block, 378f
scaling PWM input to range, 376f
solver configuration block by Simscape™, 390f
tunable electrical torque properties of DC motor block, 386f
tunable mechanical properties of DC motor block, 387f
Degree-of-freedom (DOF), 74–75
3 degrees-of-freedom robotic arm (3-DOF robotic arm), 21 , 23f
boundary diagram of, 22f
off-board diagnostics process for, 21f
Diagnostic algorithm development, 172–174
Diagnostics, 3
Digital twin model, 203
application problem, 156–160
fitting curve, 159f
physical and digital model, 161f–162f
power simulation error as function, 159f
and calibration, 260
of DC motor system, 340–341
experimental data collection for model creation, 140
Off-BD steps covered, 137f
PV hardware setup, 137–140
block diagram of solar panel system, 139
simulation results, 150–156
current and voltage measurements in system, 154f
default mask parameters, 152f
diodes added to system, 154f
editing mask, 151f
icon and ports of mask, 151f
solver configuration block, 155f
Digital twins, 1–3
developing and deploying, 4f
HEV system model deployment to cloud, 283–334
Double mass spring damper system, 112f–113f
application problem, 135
failed system, 127–131
digital twin and physical asset subsystems, 133f–134f
first and second ideal translational motion sensor blocks, 131f
flags for scope, 128f–131f
position of first mass, 132f
position of second mass, 133f
position PS-Simulink converter parameters, 129f
scope number of inputs, 130f
velocity of first mass, 132f
velocity of second mass, 132f
velocity PS-Simulink converter parameters, 128f
hardware parameters, 112
main elements of, 114f
simulation process, 112–131
damper coefficient parameter, 119f
force input for double spring +mass damper system, 118
ideal force source, 122f
ideal translation motion sensor, 120f
initial conditions and first spring+ mass damper combo, 115–116 , 119f
library block, 127f
mass parameter, 120f
motion sensor connected to R junction port, 121f
position and velocity of first spring+ mass damper combo, 116–117
PS-Simulink converter, 126f
scope for double spring +mass damper system outputs, 121–126
Simulink-PS converter block, 124f
Simulink-PS converter parameters, 125f
spring constant parameter, 119f
step force input, 124f
step input, 123f
step signal parameters, 125f
translational damper, 117f
velocity of second spring +mass damper combo, 117 , 122f
Driver SimscapeTM model development
DC motor hardware communication to AWS, 543–545
for hardware, 526–542
E
Edge computing, 19–20
EDGE device setup and cloud connectivity, 227–260
Embedded systems, 13
Engine system, 216 , 219f
Enterprise resource planning (ERP), 12–14
Equipment monitoring, 14
ESP32 Arduino software for communication between Arduino and ESP32, 426–438
copying ESP32 Arduino Library to Arduino Libraries folder, 455f
downloading ESP32 program, 440f
initializing variables used in sketch, 435f
library from GitHub, 454f
new sketch for ESP32 programming, 434f
powerup setup() function, 436f
runtime periodic loop() function, 437f
serial monitor, 441f
ESP32 module, 397–399
code upload status to, 414f
COM port, 412f
compiling and deploying sample Wi-Fi scan program, 413f
development board, 409f
ESP32 Wi-Fi controller module, 339
finished installation ESP32 Wi-Fi module board support package, 408f
hardware setup to communicating between Arduino and, 399–401
installation progress ESP32 Wi-Fi module board support package, 408f
installing ESP32 Wi-Fi module board support package, 407f
interface Arduino with ESP32 for wired serial communication, 406f
opening sample Wi-Fi scan program code, 410f
selecting and updating Arduino IDE preference for ESP32 Wi-Fi module, 406f
software setup for ESP32 programming, 404–411
updating additional board manager in preferences, 407f
F
Farming, 14–15
Fault injection, 172–174
Feedforward controller top-level subsystem, 363 , 368f
5G accelerating cloud computing, 20
Friction coefficient, 99–100
G
Gateways, 17–18
General Data Protection Regulation (GDPR), 18
Generator speed, 524–526
Generator speed piecewise functions, 524 , 525f
Google, 17
H
Hybrid Electric Vehicle (HEV), 203
application problem, 334
block diagram, 209 , 209f
deploying digital twin HEV system model to cloud, 283–334
digital twin modeling and calibration, 260
EDGE device setup and cloud connectivity, 227–260
failure modes and diagnostic concept of system, 209–211
Off-BD diagnostics process, 210f
off-BD steps, 203f
off-board diagnostics algorithm development, 260–279 , 263f
physical asset/hardware setup, 204–207 , 204f
checking Python and PIP versions, 206f
connecting to Raspberry Pi remotely using Putty, 205f
installing Mosquitto MQTT broker, 207f
installing Pah0-MQTT client for Python, 208f
Simscape™ model of system, 211–223
I
ifconfig command, 205
Infrastructure as a Service, 11
Input–output response in Wind turbine SimscapeTM model, 518
Internet of things (IoT), 1 , 13
applications, 13–18
Edge Gateways, 17–18
Hub interfacing modules, 17–18
Inverter circuit for motor drive systems, 164f–165f , 165
application problem, 174
failure modes and diagnostics concept, 165
fault injection and diagnostic algorithm development, 172–174
configuring step input block, 198f
configuring switch block, 198f
inverter output currents from simulation, 199f
inverter output voltages from simulation, 200f
open-circuit failure to inverter circuit, 197f
Off-BD diagnostics process, 166f
Off-BD steps, 163f
Simscape model, 165–171
M
Machine learning, 17
MathWorks, 1 , 5 , 342 , 347f
MATLAB®, 5–8 , 203
Central HEV model, 212
Simscape™, 46–48
Modeling, 5–8
MOSFET, 349
Mosquitto MQTT communication protocol, 207
MQTT transmit block, 233 , 235f
Multiphysics models, 5
Multiphysics simulation models, 1
O
OEMs, 14
Off-BD/prognostics algorithm development for DC motor controller hardware, 458–479
algorithm flow chart, 461f
building Simscape™ model, 464f
copy Simscape™ DC motor model and initialization M file, 462f
generated code folder and executable application, 466f
model Codegen and build progress, 465f
optimized motor model parameters, 463f
Python program to testing executable application, 479f
recompiled executable application, 476f
root mean square error values for actual and predicted motor speeds, 476f
running python validation script, 479f
sample input. csv file, 468f
Off-board diagnostics (Off-BD), 3–5 , 6f–8f , 21 , 111 , 112f , 203 , 339 , 513
algorithm development, 260–279 , 263f
SimscapeTM digital twin model deployment to AWS, 545–569
for 3-DOF robotic arm, 21f
for wind turbine system, 513f
On-board diagnostics (OBD), 3–5 , 4f
On-board digital twins, 4
P
Paho-MQTT, 207
“Pay-as-you-use” model, 11
Photovoltaic (PV)
adding offset for power, 160f
cells, 137
creating subsystem for, 149f
electrical ratings, 138t
indoor setup, 139f
modes and diagnostics concept, 139–140
circuit schematic of PV experimental setup, 140f
electrical specifications of indoor setup, 139t
outdoor setup, 138f
simulation and experimental results, 157f
with power offset, 160f–161f
solar cell modeling of, 141–143
system Simscape model, 141
Piecewise function, 524–526
Pitch actuation control logic, 521
Plant models, 216
Platform as a service, 12
Positive terminal, 141
Power split system, 216 , 219f
Prognostics, 3
Proportional Integral controller (PI controller), 212–216 , 215f
gains change, 521
Pulse-width modulated output (PWM output), 349
PWM generator, 163
Putty Desktop App, 205
Python, 205 , 206f
IoT Python SDK, 251f
program to testing executable application, 479f
Python 2.7, 205
R
Raspberry Pi hardware, 203
computer board, 204
HEV Simscape™, 221
Raspberry Pi model with MQTT transmit logic, 234f
Simulink support package installation steps, 223
updating Raspberry Pi IP address and login credentials, 223 , 231f
RoboholicManiacs, 71–72 , 72f , 341
Robotic arm, 21
application problem, 65–70
boundary diagram of 3-DOF robotic arm, 22f
hardware parameters, 23–24
measuring dimensions of robotic arm elements, 24f
off-board diagnostics process for 3-DOF robotic arm, 21f
Root Mean Square Error (RMSE), 203 , 340–341
S
S-function for ball plate interaction, 101–106
Security, 18
Self-diagnostics, 4–5
Series–Parallel Hybrid Electric Vehicle system model, 211 , 212f
Shadow IT services, 18
SimMechanicsTM , See Simscape MultibodyTM
Simple Notification Services (SNSs), 283–334 , 339 , 481f
creating SNS topic, 482f
creating subscription for SNS topic, 484f
new SNS topic details, 482f
newly created SNS topic, 483f
permission policy to new role, 494f
SimScale, 5
Simscape model for motor drive inverter system, 165–171
adding digital clock block to model, 190f
adding switches, 175f
assigning block parameter values, 192f
block parameters for N-channel IGBT, 168f
connecting all blocks, 173f
connecting diodes antiparallel to switches, 179f
connecting resistor and inductor blocks, 182f
creating subsystem with selected blocks, 174f
current sensor and voltage sensor blocks, 183f
data type conversion block, 171f
DC voltage source, 176f
diode block, 178f
electrical reference block, 187f
gate driver block, 169f
IGBT block, 168f
In1 block, 172f
inductor and resistor blocks, 181f
inverter output
currents from simulation, 195f
voltages from simulation, 196f
model configuration parameters, 194f
model to generate sinusoidal signals, 190f
parameter setting, 176f
physical modeling connection port, 173f
PS-Simulink converter, 184f
PWM generator, 191f , 193f
sample time parameter for solver block, 188f
Simulink library browser, 167f
Simulink-PS converter, 170f
solver and electrical terminator, 189f
solver block, 186f
subsystem created for switch, 174f
voltage and current sensors to model, 185f
voltage source connection to switches, 177f
Simscape™ model, 8 , 23 , 71–72 , 75–90 , 75f , 203
for ball on plate, 74–90
ball plate interaction and ball connection, 96f
ball-plate interaction subsystem, 94f
Cartesian coordinates, 74f
connecting F and B connection ports to solid, 83f
connection ports, 83f , 89f
constant angle inputs to plate, 97f
creating plate subsystem, 84f
density of plate, 82f
deploying Simscape™ digital twin model to AWS cloud, 480–499
adding trigger for Lambda function from AWS IoT, 502f
AWS IoT trigger, 503f
browse and select Lambda function Zip File package, 508f
confirmed subscriptions, 487f
creating Email subscription, 485f
creating Text/SMS subscription, 484f
default Lambda function body, 491f
digital twin deployment high level diagram, 480f
execution role, 492f
importing packages, 504f
Lambda function inline editor, 509f
launching AWS Lambda functions, 488f
Linux command line, 507f
naming new role, 496f
new execution role in Lambda function, 499f
new role created, 497f
Off-BD algorithm, 506f
optional tag for new role, 495f
pending subscription, 485f
reading input and output files, 505f
reading trigger input data, 504f
root mean squared error, 505f
running digital twin model, 504f
save Lambda function with updated execution role, 500f
saving uploaded Lambda function Package, 508f
SNSs, 481f
subscription confirmation, 486f
uploading packaged Lambda function to AWS, 507f
digital twin model for DC motor, 368–375
adding DC motor block, 385f
adding H-bridge PWM driver block, 381f
adding ideal rotational motion sensor to DC motor, 389f
adding solver configuration, mechanical, and electrical reference blocks, 393f
completed Simscape™ plant model for DC motor, 394f
controlled PWM voltage and H-Bridge block, 377f
controlled PWM voltage block input scaling setting, 380f
controlled PWM voltage block output voltage setting, 380f
controlled PWM voltage block PWM setting, 379f
DC motor block provided by Simscape™, 384f
DC Motor Simscape™ plant model configuration settings, 376f
electrical reference block, 391f
H-bridge driver block bridge parameters settings, 383f
H-bridge driver block input threshold setting, 383f
H-bridge driver block simulation mode and load settings, 382f
ideal rotational motion sensor block, 388f
mechanical rotational reference block, 392f
physical input property for PWM voltage block, 378f
scaling PWM input to range, 376f
solver configuration block, 390f
tunable electrical torque properties of DC motor block, 386f
tunable mechanical properties of DC motor block, 387f
dimensions of plate, 81f
6-DOF joint, 85f
plate and, 87f
universal joint, 88f
graphical properties of plate, 82f
gripper model in, 46–48
of HEV system, 211–223
data monitoring and logging, 221 , 221f
engine system, 216 , 219f
generator, motor, DC–DC converter, and battery system, 216 , 218f
power split system, 216 , 219f
supervisory power split controller module, 216 , 217f
target vehicle speed selection, 214f
top-level, 213f
vehicle dynamics system, 216 , 220f
vehicle speed controller module, 212
mass of sphere, 95f
mechanism configuration, 78f
model connections, 92f
MultibodyTM model, 74
Mux block connections, 91f
overall model with Plate subsystem, 85f
port location, 84f
position and velocity sensing, 88f
PS-Simulink converter, 90f
radius of sphere, 95f
setting up force, 86f
setting up torques, 87f
Simulink® Library Browser, 77f
solid bock from Body Elements library, 81f
solver configuration block, 78f
sphere shape, 96f
universal joint, 79f
actuation setting, 80f
connecting blocks to, 80f
world frame block, 77f
Simulation process of Robotic arm, 24–65
See also Robotic arm
base structure, 27–35
base properties, 30f
create subsystem for base, 36f
initial conditions applied to solid block, 31f
rigid transform block, 32f
solid library block, 29f
translation from base, 32f
translation from bottom body base properties, 33f
translation from upper body base, 34f
translation from upper body base properties, 35f
upper base, 34f
elements of robotic arm model, 25f
gripper arm, 46–50
close up of gripper, 48f
Gripper subsystem, 51f
Grippertransform properties, 50f
Metacarpal properties, 54f
metacarpal transform frames, 54f
rigid transform for metacarpal properties, 53f
transform for metacarpal, 52f
translation + aligned axis for gripper, 49f
initial conditions, 24–27 , 29f
mechanism configuration, 26f
Simulink® Library Browser, 26f
solver configuration block, 27f
world frame, 28f
motion, 50–63
changing fifth point values, 62f
default view of signal builder, 60f
extending time range, 61f
final layout of 3-DOF arm, 69f
flip signal builder and PS-Simulink converter, 58f
gain library block, 66f
gain properties, 68f
gripper signal motion, 69f
motion for upper arm revolute joint, 65f
motion for upper body revolute joint, 59f
PS-Simulink® Converter, 57f
signal builder, 56f
Simulink-PS Converter, 63f
upper arm signal motion, 66f
upper arm wrist revolute motion, 64f
for upper body revolute joint, 55f
simulation of failed system, 64–65
digital twin and physical asset subsystems, 70f
response of physical asset, 70f
upper arm, 44–46
body connected to Translation4, 45f
offset in–X for revolute joint, 43f
properties, 46f
subsystem, 47f
Translation4 properties, 44f
wrist revolute for upper arm, 42f
upper body, 35–40
revolute joint, 37f
translation and rotation from VertBody2, 39f
translation3+ rotation1 properties, 40f
upper body subsystem, 41f
Vertbody2 properties, 38f
Simulation software, 5–8
Simulink® model, 5–8 , 24 , 74 , 203
Library Browser, 26f , 77f
running DC motor in open-loop steady-state points using, 350–359
adding encoder read S-function, 356f
comparing unfiltered and filtered motor speed, 361f
configuring PWM block on Arduino digital pin 5, 355f
filtering motor speed, 360f
hardware board selection, 353f
input/output of DC Motor speed control system, 350f
motor speed calculation from encoder counts, 358f
output of Encoder block, 357f
PWM input command value vs. motor speed RPM, 359f
selecting external mode simulation on model, 355f
system ID input PWM value signal, 351f
system ID input values in repeating sequence block, 354f
system ID model solver settings, 352f
Simulink®-PS converter, 55 , 57f–58f , 63f
support package for Arduino hardware, 345f
updates for communication between Arduino and ESP32, 411–426
adding and configuring function call trigger block, 417f
adding Function Call Subsystem, 415f–416f
adding logic to output data and trigger, 428f
adding transition flow to Stateflow chart, 427f
Arduino serial transmit block to model, 430f
data and trigger line for subsystem, 433f
input ports, 418f
input/output data and trigger to Stateflow chart, 423f
making connections, 419f
Model Data Explorer Window, 424f
model explorer window, 422f
opening model explorer for defining Stateflow interface, 421f
serial port baud rate properties, 432f
Stateflow chart, 420f , 425f
Software as a Service (SaaS), 1 , 12
Solar cell modeling, 141–143 , 143f , 147f–148f
cell characteristics of default, 144f
characteristics for PV1, 145t
high-level overview of model, 142f
with inputs and output, 143f
power simulation error as function, 158f
PV subsystem, 145–149 , 145f
configuration of default, 146f
temperature dependence of default, 147f
Solar panel
diagnostic, 139
system
block diagram, 139 , 140f
Off-BD diagnostic process, 141f
Speech recognition, 17
Spring mass damper system, 111
Steady-state speed in STARTUP state, 521
Supervisory power split controller module, 216 , 217f
T
Target speed, 521 , 522f
TensorFlow services, 17
Tracking, 14
Tractive force, 216–221
U
Ubuntu, 545–553
V
Vehicle diagnostics, 4
Vehicle dynamics system, 216 , 220f
Vehicle speed controller module, 212
Virtual model, 4
W
Wind speed, 524–526
Wind turbine SimscapeTM model, 516 , 516f
customizing model as per digital twin requirements, 518–521
increasing simulation time, 521
proportional and gains change in pitch actuation control logic, 521
steady-state speed in STARTUP state, 521
target speed, 521 , 522f
turbine state transitions, 524f
input–output response, 518
model components, 517
relationship development between wind speed and generator speed, 524–526
states of wind turbine, 517f
Wind turbine system
application problem, 569–572
block diagram, 514f
Driver SimscapeTM model development, 526–545
Off-BD for, 513f
SimscapeTM digital twin model deployment to AWS, 545–569
wind turbine hardware, 515
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