ESE406/505-MEAM513: Lecture 1 Introduction to Feedback and Control

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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.

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Course Administration
ESE 406/505-MEAM 513 Control Systems Department of Electrical and Systems Engineering University of Pennsylvania
Spring 2004

• Announcements
• First class is on Tuesday January 13th 2004 in
  Towne 313 from 1200-130pm.
• 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 , jadbabai_at_seas.upenn.edu ,Office
  hours Wednesdays 200-400pm, 365 GRW Moore
  bldg.
• Lectures T- TR 1200-130pm, 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.

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Controls Course Sequence

• 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
• ESE500 Linear Systems Theory
• Detailed description of state space concepts.
• Rigorous analysis and synthesis of time invariant
  and time varying systems.
• ESE 617/MEAM 613- Nonlinear Systems
• Tools and algorithms for analysis and design of
  nonlinear control systems

Spring
Fall
4
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

System 1
System 2

• Terminology

System 2
System 1
ClosedLoop
OpenLoop
System 2
System 1
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What do these two have in common?

• Tornado

• Boeing 777

• 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

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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Control Sensing Computation Actuation
In Feedback Loop
Sense Vehicle Speed
Actuate Gas Pedal
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

• 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 dont depend on the specific values of
  b, m, or k for k sufficiently large

velocity
1 as k
0 as k
time
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Example 3 Insect Flight
SENSING
ACTUATION

• 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
Electronic transmission
Electric power steering (PAS)
Cruise control
Anti-lock brakes
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Steering
Brakes
Mirrors
Wipers
Anti-skid
Cruise control
GPS
Radio
Traction control
Headlights
Shifting
Electronic ignition
Temperature control
Seats
Electronic fuel injection
Seatbelts
Fenders
Bumpers
Airbags
Suspension (control)
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Gene networks?
essential 230   nonessential 2373  
unknown 1804   total 4407
http//www.shigen.nig.ac.jp/ecoli/pec
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essential 230   nonessential 2373  
Are these redundant?
No!
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Cartoon of E. Coli metabolism
Regulatory feedback
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Regulatory feedback
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Sensing
Actuation
Signaling
Decision
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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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(No Transcript)
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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
Satellite
Optical
Power lines
Bluetooth
ATM
Link technologies
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The Internet hourglass
Applications
Web
FTP
Mail
News
Video
Audio
ping
napster
TCP
IP
Ethernet
802.11
Satellite
Optical
Power lines
Bluetooth
ATM
Link technologies
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The Internet hourglass
Applications
IP under everything
Web
FTP
Mail
News
Video
Audio
ping
napster
TCP
IP
Ethernet
802.11
Satellite
Optical
Power lines
Bluetooth
ATM
Link technologies
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Network protocols.
Files
Files
HTTP
TCP
IP
packets
packets
packets
packets
packets
packets
Links
Sources
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Modules
Protocol stack
Files
Applications
TCP
packets
packets
packets
packets
packets
TCP packets
IP
packets
packets
packets
packets
packets
IP packets
packets
packets
packets
packets
packets
Layer 2 packets
Hardware
packets
packets
packets
packets
packets
Bits
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Animation of the protocols
Files
Files
HTTP
TCP
packets
packets
packets
packets
packets
TCP packets
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Animation of the protocols
Files
Files
HTTP
TCP
IP
packets
packets
packets
packets
packets
packets
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Animation of the protocols
Files
Files
HTTP
TCP
IP
packets
packets
packets
packets
packets
packets
packets
packets
packets
packets
IP packets
IP packets
Links
Sources
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Application
Application
Application

• Each layer can evolve independently provided
• Follow the rules
• Everyone else does good enough with their layer

Vertical decomposition Protocol Stack
TCP
TCP
TCP
IP
IP
IP
IP
IP
Routing Provisioning
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Application
Application
Application
TCP
TCP
TCP
Horizontal decomposition Each level is
decentralized and asynchronous
IP
IP
IP
IP
IP
Routing Provisioning
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• Entirely different from the telephone system,
  although the parts are essentially identical
  (VLSI, copper, and fiber)
• The Internet is much more like biology and
  relies on feedback regulation at every level.
• Only recently has a coherent theory of the
  Internet started to emerge and pay off.

Application
Application
Application
TCP
TCP
TCP
Vertical decomposition
IP
IP
IP
IP
IP
Horizontal decomposition
Routing Provisioning
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Internet
Interface
Application
Application
TCP
Operating System
TCP
Simplify
IP
IP
Computer
Board
Link
Device
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Links
Sources
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Routers
Hosts
packets
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Files
Routers
Hidden from the user
Hosts
packets
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Routers
Hosts
packets
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Routers
Hosts
packets
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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)

• 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 Tue/Thur
1 Introduction to Feedback and Control
2-3 System Modeling/Analysis, Review of ODEs, and Laplace Transform
4-5 Stability and Performance
6-7 Tests for stability
8-9 Root locus analysis. Design for time domain specs.
10-11 Frequency Domain Design Bode plot.
12-14 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

• Control
• Sensing Computation Actuation
• Feedback Principles
• Robustness to Uncertainty
• Design of Dynamics

Sense
Actuate
Compute

• Many examples of feedback and control in natural
  engineered systems

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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
• jadbabai_at_seas.upenn.edu
• Course website
• on Blackboard