Transcript ESE406/505-MEAM513: Lecture 1 Introduction to Feedback and Control Ali Jadbabaie January 11, 2005 Goals:  Give an overview of the course; describe course structure, administration 

ESE406/505-MEAM513: Lecture 1
Introduction to Feedback and Control
Ali Jadbabaie
January 11, 2005
Goals:
 Give an overview of the course; describe course structure, administration
 Define feedback/control systems and learn how to recognize main features
 Describe what control systems do and the primary principles of control
Reading (available on course web page):
 Astrom and Murray, Analysis and Design of Feedback Systems, Ch 1
 “For the Spy in the Sky, New Eyes”, NY Times, June 2002.
Course Administration
Announcements :
•First class is on Tuesday January 13th 2004 in Towne 313 from 12:00-1:30pm.
Course Description: This course is an introduction to analysis and design of feedback control systems,
including classical control theory in the time and frequency domain. Modeling of physical, biological and
information systems using linear and nonlinear differential equations. Stability and performance of interconnected systems,
including use of block diagrams, Bode plots, Nyquist criterion, and Design of feedback controllers.
Suggested pre-requisites: Basic course on ordinary differential equations and linear algebra.
For Systems Engineering Students: knowledge of ESE 210 (SYS 200) material.
For EE students: Knowledge of signals and systems (ESE 325)
Instructor:
•Ali Jadbabaie , [email protected] ,
Office hours : Wednesdays 2:00-4:00pm, 365 GRW Moore bldg.
Lectures: T- TR 12:00-1:30pm, Towne 313.
Textbook:
•Feedback Control of Dynamic Systems, by Franklin, Powell and Emami Naieni, 4th Edition, Prentice Hall, 2002.
Other References:
•Modern Control Engineering, 4th Edition, by K. Ogata, Prentice Hall, 2001
•Modern Control Systems, 9th Edition, by Dorf and Bishop, Prentice Hall, 2001.
•Automatic Control Systems, by B. Kuo, Prentice Hall, 1995.
Course Notes and Links Reading material for the class will be posted on blackboard
Required reading sources
•R. M. Murray (ed), Control in an Information Rich World: Report of the Panel on Future Directions in Control,
Dynamics, and Systems, SIAM, 2002. Available online at http://www.cds.caltech.edu/~murray/cdspanel/
•K. J. Åström and Richard M. Murray, Analysis and Design of Feedback Systems, Preprint, 2004. Online access on blackboard
•J. Doyle, B. Francis, and A. Tannenbaum, Feedback Control Theory, McMillan, 1992. Online access on blackboard
•Grading : Homeworks : 20% Midterm I: 35% Midterm II : 45%
•Teaching assistants: Nima Moshtagh , Ali Ahmadzadeh
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Controls Course Sequence
Fall
ESE500 – Linear Systems Theory
 Detailed description of state space concepts.
 Rigorous analysis and synthesis of time invariant and time varying
systems.
Spring
ESE406/505-MEAM513 – Introduction to the principles and tools of
control and feedback
 Summarize key concepts, w/ examples of fundamental principles at work
 Introduce MATLAB-based tools for modeling, simulation, and analysis
 Introduction to control design
 Provide knowledge to work with control engineers in a team setting
ESE 617/MEAM 613- Nonlinear Systems
• Tools and algorithms for analysis and design of nonlinear control systems
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What is Feedback?
Miriam Webster:
the return to the input of a part of the
output of a machine, system, or
process (as for producing changes in
an electronic circuit that improve
performance or in an automatic control
device that provide self-corrective
action) [1920]
Feedback = mutual interconnection
of two (or more) systems
 System 1 affects system 2
 System 2 affects system 1
 Cause and effect is tricky; systems
are mutually dependent
Feedback is ubiquitous in natural
and engineered systems
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System 1
System 2
Terminology
System 1
System 1
System 2
System 2
Closed
Loop
Open
Loop
3
What do these two have in common?
Boeing 777
Tornado
• Highly nonlinear, complicated dynamics!
• Both are capable of transporting goods and people over long distances
BUT
• One is controlled, and the other is not.
• Control is “the hidden technology that you meet every day”
• It heavily relies on the notion of “feedback”
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Example #1: Flyball Governor
“Flyball” Governor (1788)
 Regulate speed of steam engine
 Reduce effects of variations in load
(disturbance rejection)
 Major advance of industrial revolution
Balls fly out
as speed
increases,
Valve closes,
slowing engine
Steam
engine
Boulton-Watt steam engine
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Flyball
governor
http://www.heeg.de/~roland/SteamEngine.html
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Other Examples of Feedback
Biological Systems
 Physiological regulation (homeostasis)
 Bio-molecular regulatory networks
Environmental Systems
 Microbial ecosystems
 Global carbon cycle
Financial Systems
 Markets and exchanges
 Supply and service chains
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ESE
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Control = Sensing + Computation + Actuation
In Feedback “Loop”
Actuate
Sense
Gas Pedal
Vehicle Speed
Compute
Control “Law”
Goals
 Stability: system maintains desired operating point (hold steady speed)
 Performance: system responds rapidly to changes (accelerate to 65 mph)
 Robustness: system tolerates perturbations in dynamics (mass, drag, etc)
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A modern Feedback Control System
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Two Main Principles of Control
Robustness to Uncertainty through
Feedback
 Feedback allows high performance in the
presence of uncertainty
 Example: repeatable performance of
amplifiers with 5X component variation
 Key idea: accurate sensing to compare
actual to desired, correction through
computation and actuation
Design of Dynamics through Feedback
 Feedback allows the dynamics of a
system to be modified
 Example: stability augmentation for highly
agile, unstable aircraft
 Key idea: interconnection gives closed
loop that modifies natural behavior
X-29 experimental aircraft
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Example #2: Cruise Control
disturbance
reference
mv  bv  uengine  uhill
uengine  k (vdes  v )
velocity
vdes
vss 
k
1
vdes 
uhill
bk
bk
 1 as
k 
 0 as
k 
time
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+
-
Control
+
System
Stability/performance
 Steady state velocity approaches
desired velocity as k  
 Smooth response; no overshoot or
oscillations
Disturbance rejection
 Effect of disturbances (hills)
approaches zero as k  
Robustness
 Results don’t depend on the specific
values of b, m, or k for k sufficiently
large
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Example #3: Insect Flight
SENSING
neural
superposition
eyes
hind wing
gyroscopes
(halteres)
specialized
“power”
muscles
two wings
(di-ptera)
ACTUATION
COMPUTATION
~500,000 neurons
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More information:
 M. D. Dickinson, Solving the mystery of
insect flight, Scientific American, June
2001.
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EXAMPLE # 4: Coordinated Control of Manned
and Unmanned Systems
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Other
Examples
Temperature control
Air bags
EGR control
Active
suspension
Electronic fuel injection
Electronic ignition
Electric power
steering (PAS)
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Anti-lock brakes
Electronic
transmission
Cruise control
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Steering
Brakes
Anti-skid
Cruise control
Traction control
Shifting
Electronic ignition
Wipers Mirrors
GPS
Temperature control
Electronic fuel injection
Seatbelts
Bumpers Fenders
Suspension (control) Airbags
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Radio
Headlights
Seats
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Gene networks?
essential:
nonessential:
unknown:
total:
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230
2373
1804
4407
http://www.shigen.nig.ac.jp/ecoli/pec
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essential:
nonessential:
230
2373
Are these
“redundant?”
No!
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Cartoon of E.
Coli metabolism
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Regulatory
feedback
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Regulatory
feedback
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Actuation
Decision
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Sensing
Signaling
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Organized complexity
Simple behavior
Robust and adaptive
Evolvable
Enormous “hidden” complexity
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Segway: The human Transporter
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Modern Engineering Applications of Control
Flight Control Systems
 Modern commercial and military
aircraft are “fly by wire”
 Autoland systems, unmanned
aerial vehicles (UAVs) are already
in place
Robotics
 High accuracy positioning for
flexible manufacturing
 Remote environments: space, sea,
non-invasive surgery, etc.
Chemical Process Control
 Regulation of flow rates,
temperature, concentrations, etc.
 Long time scales, but only crude
models of process
Communications and Networks
 Amplifiers and repeaters
 Congestion control of the Internet
 Power management for wireless
communications
Automotive
 Engine control, transmission
control, cruise control, climate
control, etc
 Luxury sedans: 12 control devices
in 1976, 42 in 1988, 67 in 1991
AND MANY MORE...
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The Internet: Largest feedback system built by man
Applications
Web
FTP
Mail
News
Video
Audio
ping
napster
Transport protocols
TCP SCTP UDP
ICMP
IP
Ethernet 802.11
Power lines ATM
Optical
Satellite Bluetooth
Link technologies
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The Internet hourglass
Applications
Web
FTP
Mail
News
Video
Audio
ping
napster
TCP
IP
Ethernet 802.11
Power lines ATM
Optical
Satellite Bluetooth
Link technologies
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The Internet hourglass
Applications
Web
FTP
Mail
News
Video Audio
IP under
everything
ping
napster
TCP
IP
Ethernet 802.11
IP on
Power lines ATM Optical
everything
Satellite Bluetooth
Link technologies
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Network protocols.
Files
HTTP
Files
TCP
IP
packets
packets
packets
packets
packets
packets
Links
Sources
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Protocol stack
Applications
TCP
IP
Hardware
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Modules
Files
packets
packets
packets
packets
packets
TCP
packets
packets
packets
packets
packets
IPpackets
packets
packets
packets
packets
packets
Layerpackets
2 packets
packets
packets
packets
packets
packets
Bits
28
Animation of the protocols
Files
HTTP
Files
TCP
packets
packets
packets
packets
packets
TCP
packets
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Animation of the protocols
Files
HTTP
Files
TCP
packets
packets
packets
packets
packets
TCP
packets
IP
packets
packets
packets
packets
packets
TCP
packets
packets
packets
packets
packets
packets
packets
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Animation of the protocols
Files
HTTP
Files
TCP
packets
packets
packets
packets
packets
TCP
packets
packets
packets
packets
packets
IPpackets
packets
IP
packets
packets
packets
packets
packets
TCP
packets
packets
packets
packets
packets
IPpackets
packets
packets
packets
packets
packets
packets
Layer
2 packets
Links
Sources
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packets
packets
packets
packets
packets
Bits
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TCP
IP
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Vertical decomposition
Protocol Stack
Application
IP
Application
Application
Each layer can evolve
TCP
TCP
independently provided:
1. Follow the rules
2. Everyone else does
IP
IP with IP
“good enough”
their layer
Routing
Provisioning
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Application
Application
TCP
TCP
IP
Application
IP
IP
TCP
IP
IP
Horizontal decomposition
Each level is decentralized and asynchronous
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Routing
Provisioning
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Vertical decomposition
• Entirely different from the telephone system,
although the parts are essentially identical (VLSI,
Application
Application
Application
copper, and fiber)
• The Internet is much more like biology and
TCPrelies on feedbackTCP
TCP
regulation at every level.
• Only recently has a coherent theory of the
Internet started to emerge and pay off.
IP
IP
IP
IP
IP
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Routing
Horizontal
decomposition
Provisioning
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Internet
Interface
Application
Application
TCP
TCP
Operating
System
Simplify
IP
IP
Computer
Board
Device
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Link
35
Links
Sources
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Routers
packets
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Hosts
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Files
Routers
Hidden from the user
packets
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Hosts
38
Routers
packets
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Hosts
39
Routers
packets
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Hosts
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Control Tools
Modeling
 Input/output representations for subsystems +
interconnection rules
 System identification theory and algorithms
 Theory and algorithms for reduced order modeling
+ model reduction
Analysis
 Stability of feedback systems, including
robustness “margins”
 Performance of input/output systems (disturbance
rejection, robustness)
Synthesis
 Constructive tools for design of feedback systems
 Constructive tools for signal processing and
estimation (Kalman filters)
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MATLAB Toolboxes
 SIMULINK
 Control System
 Neural Network
 Data Acquisition
 Optimization
 Fuzzy Logic
 Robust Control
 Instrument Control
 Signal Processing
 LMI Control
 Statistics
 Model Predictive Control
 System Identification
 µ-Analysis and
Synthesis
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Magic of Feedback
• Feedback is used to regulate the value of a quantity in a system to a desired
level, by measuring the error, i.e., difference between the desired value and the
sensed value.
•Sometimes the decision is based on the instantaneous value of error, and
sometimes is based on the history of the error, and/or predictions on the future
value of the error. Some times we use all three.
•The performance of a feedback system is measured based on the response to a
“step” change in the reference, or in tracking a sinusoid.
• Feedback regulation will work even when the “components” are uncertain.
• The down side of using feedback is that
 It can cause instability
 It makes the design more complicated
• The main components of a feedback loop are sensing, decision/computation, and
actuation.
• We will use theory of differential equations, linear algebra and complex variables
to analyze feedback systems.
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Overview of the Course
Wk
1
2-3
Tue/Thur
Introduction to Feedback and Control
System Modeling/Analysis,
Review of ODEs, and Laplace Transform
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4-5
Stability and Performance
6-7
Tests for stability
8-9
Root locus analysis. Design for time
domain specs.
1011
Frequency Domain Design: Bode plot.
1214
Loop Analysis of Feedback Systems.
Nyquist criterion
15
Fundamental Limits on Performance
16
Uncertainty Analysis and Robustness
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Summary: Introduction to Feedback and Control
Actuate
Sense
Control =
Sensing + Computation +
Actuation
Feedback Principles
 Robustness to Uncertainty
 Design of Dynamics
Compute
Many examples of feedback and control in natural & engineered systems:
BIO
ESE
BIO
ESE
CS
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Summary
Feedback control is Every where
you just have to look for it
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Welcome to
ESE406/505- MEAM513
Control Systems
Instructor: Ali Jadbabaie
[email protected]
Course website:
on Blackboard
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