# System Specifications - Electrical Engineering

Τεχνίτη Νοημοσύνη και Ρομποτική

14 Νοε 2013 (πριν από 7 χρόνια και 9 μήνες)

305 εμφανίσεις

1

Dan O.
Popa, EE 1205 Intro. to EE

1

Systems Concepts

Dan Popa, Ph.D., Associate Professor

popa@uta.edu
,
http://ngs.uta.edu

Systems
Approach
and Related Concepts

Modeling
: Physical, mathematical

System
identification, block diagrams, subsystems, modules,
interconnection

Input/output, environmental effects, linear
-
nonlinear, dynamic,
causal
-
noncausal

Examples of complex robotic systems

2

Dan O.
Popa, EE 1205 Intro. to EE

2

Signals and Systems

Signal:

Any time dependent physical quantity

Electrical, Optical, Mechancal

System:

Object in which input signals interact to
produce output signals.

Some have fundamental properties that make
it predictable:

Sinusoid in, sinusoid out of same frequency (when
transients settle)

Double the amplitude in, double the amplitude out
(when initial state conditions are zero)

?
x
(
t
)
u
(
t
)
y
(
t
)
3

Dan O.
Popa, EE 1205 Intro. to EE

3

Signal Classification

Continuous Time vs.
Discrete Time

Telephone line signals,
Neuron synapse
potentials

Stock Market, GPS
signals

Analog vs. Digital

waves, battery power

Computer signals, HDTV
images

4

Dan O.
Popa, EE 1205 Intro. to EE

4

Signal Classification

Deterministic vs. Random

Background Noise Speech
Signals

Periodic vs. Aperiodic

Sine wave

Sum of sine waves with non
-
rational frequency ratio

5

Dan O.
Popa, EE 1205 Intro. to EE

5

System Classification

Linear vs. Nonlinear

Linear systems have the property of
superposition

If U
→Y,
U1
→Y1,
U2
→Y2 then

»
U1+U2
→ Y1+Y2

»
A*U
→A*Y

Nonlinear systems do not have this property,
and the I/O map is represented by a
nonlinear mapping.

Examples: Diode, Dry Friction, Robot Arm at
High Speeds.

Memoryless vs. Dynamical

A memoryless system is represented by a
static (non
-
time dependent) I/O map:
Y=f(U).

Example: Amplifier

Y=A*U, A
-

amplification
factor.

A dynamical system is represented by a
time
-
dependent I/O map, usually a
differential equation:

Example: dY/dt=A*u, Integrator with Gain A.

Mandelbrot set, a fractal image,
result of a Nonlinear Discrete
System Z
n+1
=Z
n
²+C

0
0
)
sin(
2
2
2
2

L
g
dt
d
L
g
dt
d
Exact Equation,
nonlinear

Approximation
around vertical
equilibrium, linear

6

Dan O.
Popa, EE 1205 Intro. to EE

6

System Classification

Time
-
Invariant vs. Time Varying

Time
-
invariant system parameters do not change over time. Example: pendulum, low power
circuit

Time
-
varying systems perform differently over time. Example: human body during exercise.

Causal vs. Non
-
Causal

For a causal system, outputs depend on past inputs but not future inputs.

Examples: most
engineered and natural systems

A non
-
causal system, outputs depend on future inputs. Example: computer simulation
where we know the inputs a
-
priori, digital filter with known images or signals.

Stable vs. Unstable

For a stable system the output to bounded inputs is also bounded. Example: pendulum at
bottom equilibrium

For an unstable system the ouput diverges to infinity or to values causing permanent
damage. Example: short circuit on AC line.

7

Dan O.
Popa, EE 1205 Intro. to EE

7

System Modeling

Building mathematical models based on observed
data, or other insight for the system.

Parametric models (analytical): ODE, PDE

Non
-
parametric models: graphical models
-

plots,
look
-
up cause
-
effect tables

Mental models

Driving a car and using the
cause
-
effect knowledge

Simulation models

Many interconnect
subroutines, objects in video game

8

Dan O.
Popa, EE 1205 Intro. to EE

8

Types of Models

White Box

derived from first principles laws: physical,
chemical, biological, economical, etc.

Examples: RLC circuits, MSD mechanical models
(electromechanical system models).

Black Box

model is entirely derived from measured data

Example: regression (data fit)

Gray Box

combination of the two

9

Dan O.
Popa, EE 1205 Intro. to EE

9

White Box Systems: Electrical

Defined by Electro
-
Magnetic Laws of Physics:
Ohm’s Law, Kirchoff’s Laws, Maxwell’s Equations

Example: Resistor, Capacitor, Inductor

u
R
i
u
i
C
u
i
L
10

Dan O.
Popa, EE 1205 Intro. to EE

10

RLC Circuit as a System

Kirchoff’s Voltage Law (KVL):

u
1
L
C
R
u
u
3
u
2
RLC
q
(
t
)
u
(
t
)
i
(
t
)
11

Dan O.
Popa, EE 1205 Intro. to EE

11

White Box Systems: Mechanical

Newton’s Law:

M
K
B
F
MSD
x
(
t
)
F
(
t
)
x
(
t
)
Mechanical
-
Electrical

Equivalance:

F (force)

~V (voltage)

x

(displacement) ~ q (charge)

M (mass) ~ L (inductance)

B (damping) ~ R (resistance)

1/K (compliance) ~ C (capacitance)

12

Dan O.
Popa, EE 1205 Intro. to EE

12

White
-
Box vs. Black
-
Box Models

Newton
-
Euler

Law
:

Lawn
Mower
x
,
y
,
θ
ω
_
r
(
t
)
,
ω
_
l
(
t
)
X
(
t
)
,
Y
(
t
)
Θ
(
t
)

13

Dan O.
Popa, EE 1205 Intro. to EE

13

Grey
-
Box Models

14

Dan O.
Popa, EE 1205 Intro. to EE

14

White Box vs Black Box Models

White

Box Models

Black
-
Box Models

Information Source

First Principle

Experimentation

Good Extrapolation

Good

understanding

High reliability, scalability

Short time to develop

Little domain expertise
required

Works for not well
understood systems

Time consuming

and
detailed domain expertise
required

Not

scalable, data restricts
accuracy, no system
understanding

Application Areas

Planning, Construction,
Design, Analysis,

Simple
Systems

Complex processes

Existing systems

Start to understand simple white continuous time models
which are
linear

Eventually deal with grey
-
box or black
-
box models in real
-
life

15

Dan O.
Popa, EE 1205 Intro. to EE

15

Linear vs. Nonlinear

Why study continuous linear analysis of signals and
systems when many systems are nonlinear in
practice?

Linear systems have generic, predictable performance.

Nonlinear systems can be approximated and transformed
into linear systems.

S
ome techniques for analysis of nonlinear systems are
based on linear methods

If you don’t understand linear dynamical systems you
certainly can’t understand nonlinear systems

16

Dan O.
Popa, EE 1205 Intro. to EE

16

Application Areas for Systems Thinking

Classical circuits & systems (1920s

1960s) (transfer
functions, state
-
space description of systems).

First engineering applications: military
-

aerospace 1940’s
-
1960s

Transitioned from specialized topic to ubiquitous in 1980s
with EE applications to:

Electronic circuit design

Signal and image processing

Networks (wired, wireless), imaging, radar, optics.

Control of dynamical systems

Feedback control, prediction/estimation/identification of systems, robotics, micro
and nano systems

17

Dan O.
Popa, EE 1205 Intro. to EE

17

Diagram Representation of Systems

Top

Bottom 1

Bottom 2

Bottom 3

Middle

Graph
Node 1

Graph
Node 3

Graph
Node 5

Graph
Node 4

Graph
Node 2

Hierarchical Diagram: Organizations

Undirected Graph: Networks

Flowchart: Procedures, Software

18

Dan O.
Popa, EE 1205 Intro. to EE

18

System Simulation Software

http://www.mathworks.com/support/2010b/simu

National Instruments Labview

http://www.ni.com/gettingstarted/labviewbasics/
environment.htm

19

Dan O.
Popa, EE 1205 Intro. to EE

19

EE
-
Specific Diagrams

Block Diagram Model:

Helps understand flow of information (signals) through a complex system

H
elps visualize I/O dependencies

E
quivalent to a set of linear algebraic equations.

Based on a set of primitives:

Transfer Function

Summer/Difference

Pick
-
off point

Signal Flow Graph (SFG):

Directed Graph alternative

H
(
s
)
U
(
s
)
Y
(
s
)
+

+

U2

U1

U1+U2

U

U

U

20

Dan O.
Popa, EE 1205 Intro. to EE

20

EE
-
Specific Diagrams: Signal Flow Graph
(SFG

Directed Graph)

2
-
port circuit SFG

Multi
-
loop Control SFG

21

Dan O.
Popa, EE 1205 Intro. to EE

21

EE
-
Specific Diagrams:

Block Diagram Simplification Rules

22

Dan O.
Popa, EE 1205 Intro. to EE

22

EE
-
Specific Diagrams:

Block Diagram Reduction Rules

23

Dan O.
Popa, EE 1205 Intro. to EE

23

EE
-
Specific Diagrams:

Block Diagram Reduction Rules

24

Dan O.
Popa, EE 1205 Intro. to EE

24

Robots as Complex Systems

G. Bekey definition: an entity that can sense, think and act.

Extensions: communicate, imitate, collaborate

Classification: manipulators, mobile robots, mobile manipulators.

Sense

Think

Act

Robot

25

Dan O.
Popa, EE 1205 Intro. to EE

Research in Multiscale Robotics at

Next Gen Systems (NGS) Group

Robotics

Control Systems

Manufacturing &
Automation

Established Technologies

Emerging Technologies

Micromanufacturing

Microrobotics

Microassembly

Micropackaging

Sensors & Actuators

NanoManufacturing

Microsystems &
MEMS

Nanotechnology

Biotechnology

Small
-
scale

Robotics &

Manufacturing

Modeling & Simulation

Control Theory

Algorithms

Tools and Fundamentals

Sensor networks

Surgical robotics

Human
-
like robots

Distributed systems

New applications

for small
-
scale systems

26

Dan O.
Popa, EE 1205 Intro. to EE

26

NGS Research

Micro and Nano Robotics

Manufacturable Micro and Nano Robotics

Automated MEMS Assembly and Packaging

Mobile Microrobotics

Sub
-
Millimeter size robots powered by ambient fields

Next Generation Robotics for Healthcare

Assistive Robotics

Treatment of cognitive and motor disabilities (Autism, CP) using
-
Robot Interaction (HRI)

Microrobotics for healthcare application (in
-
vivo or in
-
vitro manipulation
and process tools)

Examples from recent projects

Micro Robotic Factories

UTA Microrobotics Team

Zeno and Neptune Assistive Robots

27

Dan O.
Popa, EE 1205 Intro. to EE

27

-

Multiscale (Macro
-
Micro)

Robotic Assembly Cell

Multiscale Robotic Workcell: work volume of approximately O(1
m
3
), robots with dimensions of O(10
-
1
~10
-
2
m), handles parts of
size O(10
-
2
~10
-
4
m), and achieves accuracies in the scale of O(10
-
4
~10
-
6
m).

Four precision robots sharing a common workspace, with multiple
end
-
effectors: microgrippers, zoom microscope, laser for solder
reflow.

Control through Labview and NI motion control products

Laser solder reflow

(delivery optics)

3DOF

Zoom
-
camera system

2DOF

Gripper Manipulator

4DOF

Tool tray with

quickchange

end
-
effectors

Parts tray

Fine manipulator

3DOF

Hot plate for die attach

Packaged MOEMS device

using the M³ for US Navy

Schematic and Control System Diagram of M³

D.O. Popa, R. Murthy, A. N. Das, “M3
-

Deterministic, Multiscale, Multirobot Platform for Microsystems Packaging: Design and Qua
si
-
Static Precision Evaluation,” in IEEE Transactions on Automation Science and Engineering (T
-
ASE), April 2009.

28

Dan O.
Popa, EE 1205 Intro. to EE

28

-

Wafer Scale Microfactory

(Micro
-
Nano)

“From a few robots+controllers to many µrobots via assembly and die bonding”

D
.

O
.

Popa,

R

Murthy,

A
.
N
.

Das,

“M³,

μ³,

and

:

Top
-
down,

Deterministic

Macro

to

Nano

Robotic

Factories

with

Yield

and

Speed

Precision

Metrics,”

in

Proc
.

of

2008

Int’l

Workshop

on

Microfactories

(IWMF

08
),

Evanston,

Illinois,

Oct
.

6
-
8
,

2008
.

Controller

+ Robot

µparts, nparts in

µparts, nparts in

Assemblies

out

µrobot MEMS dies

µcontroller IC dies

29

Dan O.
Popa, EE 1205 Intro. to EE

29

AFAM: A Millimetric Assembled 4 axis
Micromanipulator for N³

Microrobot volume:2mm x 3mm x 1mm

Work Volume: 50

m x 50

m x 75

m

Actuation: Electrothermal

Transmission: XY

direct drive



-

cable driven

All components fabricated using DRIE on 50~100 microns (device) SOI.

Arm is detethered and assembled out
-
of
-
plane using passive jammer.

Cable (
30

m Cu wire)

is cut to required length and assembled.

R. Murthy, D. O. Popa, “A Four Degree Of Freedom Microrobot with Large Work Volume”, in
proc. of IEEE ICRA ‘09, Kobe, Japan, May 2009

30

Dan O.
Popa, EE 1205 Intro. to EE

30

ARRIpede: a Millimetric Microcrawler

System Specifications:

Volume = 1.7cm X 1.7cm X 1 cm

Weight ~ 4g (with battery)

Velocity=~2mm/s

Resolution of 20~30nm

Repeatability better than 12
μ
m

Continous operation: 10 minutes at max speed, 100
minutes at .1x max speed

Rakesh

Murthy,

A

.
N
.

Das,

D
.

O
.

Popa,

“Nonholonomic

Control

of

an

Assembled

Microcrawler,”

in

Proc
.

Of

9
th

International

IFAC

Symposium

on

Robot

Control,

Gifu,

Japan,

September

2009
.

31

Dan O.
Popa, EE 1205 Intro. to EE

31

-

Wafer Scale Microfactory for
Nanotechnology

32

Dan O.
Popa, EE 1205 Intro. to EE

32

Making the Microfactory by Automated
3D Microassembly

Control Challenges:

-
Larger number of robots

-

Measurement uncertainty, measurement range,

-

Time delays

-

Fewer embedded sensors, low SNR

-

Manufacturing uncertainty, inacurate robot models)

-

Environmental effects (stiction, temperature)

33

Dan O.
Popa, EE 1205 Intro. to EE

33

NIST Microbotics Challenge 2011

Hosted at IEEE International Conference on Robotics and Automation, Shanghai, China, May 10, 2011.

7 Qualified Teams: France (FEMTO
-
ST), Italy (IIT), Univ. of Waterloo (CA), 4 US Universities (Stevens,
Hawaii, Maryland, UTA

Maximum robot size: 600 microns sphere.

Mobility

Challenge

Micro

Assembly

Event

Vibration and
Laser Actuated

UTA Microrobots, 2011

34

Dan O.
Popa, EE 1205 Intro. to EE

34

UTA Vibot Control Using

National Instruments PXI
-
8196

Microrobot pose (x, y,
θ
) from NI
-
1742 Smart Camera

Exchange of pose data with the control interface VI via shared variables

User control of square wave output through PXI
-
5201 Arbitrary Waveform
Generator (AWG). Output frequency to piezoelectric actuator. PXI 7831 FPGA RIO

Data logging via control interface VI

UTA Microrobotics Team video

square wave

amplitude & frequency

PXI
-
8196 controller

robot

pose

x, y,
θ

PZT

Actuator

arena and

microrobot

image

user control

control interface VI

35

Dan O.
Popa, EE 1205 Intro. to EE

35

-
Robot Interaction for Assistance to Children with Special Needs

Interaction
t
hrough advanced vision, motion control, gestures and compliant robotic skin.

Interaction though Wii Remotes, Neural Brain interfaces and iPad devices.

Collaboration with Heracleia Lab, UTA, Hanson Robotics Inc., UNTHSC

MicroRobotics for In
-
Vivo Diagnosis and Treatment

Mobile microrobotics: Sub
-
mm size robots powered and controlled wirelessly through wireless energy fields such as EM,
vibration, or laser.

Micro and Nano Assembly of Surgical Instruments for medical procedures in hard to reach places (eye, ear, brain).

Next
Generation Robots for Healthcare

Past NGS US Navy

Micro Fuze using MEMS technology, hermetically
packaged for 30 year extended life applications

Interface
Wii Remote
PC
VS Code
Neptune Mobile
Manipulator
Encoders
Force Sensors
Camera
Recorded Results
Patient
WiFi
Bluetooth
Advanced control for Zeno Robokind (by Hanson Robotics,
Inc.) generating facial expressions and maintaining eye
contact with NGS programmers.

Neptune Mobile Manipulator interfaced to human users
through Wii Remote, Neural Headband, and Cameras, and
provides assistance to users with motor impairments.

Laser
-
driven microrobots using MEMS, and a schematic diagram of experimental instrument designed by UTA’s team for 2011 US NIST
Microrobotics Challenge Competition

36

Dan O.
Popa, EE 1205 Intro. to EE

Realistic &
Intuitive
Human
-
Robot
Interaction

Co
-
botics w/
Physical
Interaction

Real
-
Time
Visual
Feedback

and Facial
Expressions

Human
-
Robot
Interfaces

Advanced Human Robot Interaction

Zeno Video

Neptune Control through

Robot Touch HRI

Visual HRI

37

Dan O.
Popa, EE 1205 Intro. to EE

Conclusion

-
Abstraction is the basis for system level thinking. Abstraction
requires advanced mathematics, and it is especially required of
Electrical and Computer Engineers.

-
Systems are composed of building blocks used to generalize and
manage complexity.

-
Modeling and simulations are important building tools in systems
-
level approaches.

-
System
-
level concepts are important tools in your engineering
education.

-
System
-
level thinking helps manage

complexity of present
-
day
technology, economics
, society.

-
System
-
level approach originating in EE are a great advantage in
interdisciplinary projects with all your future colleagues from other
departments.