1. 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.

2. January 11, 20051 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 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, jadbabai@seas.upenn.edu, Office hours : Wednesdays 2:00-4:00pm, 365 GRW Moore bldg.Ali Jadbabaiejadbabai@seas.upenn.edu 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/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 blackboardOnline access J. Doyle, B. Francis, and A. Tannenbaum, Feedback Control Theory, McMillan, 1992. Online access on blackboardOnline access Grading : Homeworks : 20% Midterm I: 35% Midterm II : 45% Teaching assistants: Nima Moshtagh, Ali AhmadzadehNima MoshtaghAli Ahmadzadeh

3. January 11, 20052 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. January 11, 20053 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 Terminology System 2 System 1 System 2System 1 System 2System 1 Closed Loop Open Loop

5. January 11, 20054 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”

6. January 11, 20055 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 http://www.heeg.de/~roland/SteamEngine.html Boulton-Watt steam engine Flyball governor Steam engine

7. January 11, 20056 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 ESE

8. January 11, 20057 Control = Sensing + Computation + Actuation Sense Vehicle Speed Compute Control “Law” Actuate Gas Pedal In Feedback “Loop” 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)

9. January 11, 20058 A modern Feedback Control System

10. January 11, 20059 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

11. January 11, 200510 Example #2: Cruise Control ControlSystem ++ - disturbance reference 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 time velocity  1 as k   0 as k 

12. January 11, 200511 Example #3: Insect Flight More information:  M. D. Dickinson, Solving the mystery of insect flight, Scientific American, June 2001. ACTUATION two wings (di-ptera) specialized “power” muscles SENSING neural superposition eyes hind wing gyroscopes (halteres) COMPUTATION ~500,000 neurons

13. January 11, 200512 EXAMPLE # 4: Coordinated Control of Manned and Unmanned Systems

14. January 11, 200513 Other Examples Cruise control Electronic ignition Temperature control Electronic fuel injection Anti-lock brakes Electronic transmission Electric power steering (PAS) Air bags Active suspension EGR control

15. January 11, 200514 Brakes Airbags Seatbelts MirrorsWipers Headlights Steering GPS Radio Shifting Traction control Anti-skid Electronic ignition Electronic fuel injection Temperature control Cruise control Bumpers Fenders Suspension (control) Seats

16. January 11, 200515 essential : 230 nonessential:2373 unknown:1804 total:4407 http://www.shigen.nig.ac.jp/ecoli/pec Gene networks?

17. January 11, 200516 essential : 230 nonessential:2373 Are these “redundant?” No!

18. January 11, 200517 Regulatory feedback Cartoon of E. Coli metabolism

19. January 11, 200518 Regulatory feedback

20. January 11, 200519 Decision SensingActuation Signaling

21. January 11, 200520 Organized complexity Simple behavior Robust and adaptive Evolvable Enormous “hidden” complexity

22. January 11, 200521 Segway: The human Transporter

23. January 11, 200522

24. January 11, 200523 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...

25. January 11, 200524 The Internet: Largest feedback system built by man IP WebFTPMailNewsVideoAudiopingnapster Applications TCPSCTPUDPICMP Transport protocols Ethernet 802.11SatelliteOpticalPower linesBluetoothATM Link technologies

26. January 11, 200525 The Internet hourglass IP WebFTPMailNewsVideoAudiopingnapster Applications TCP Ethernet 802.11SatelliteOpticalPower linesBluetoothATM Link technologies

27. January 11, 200526 The Internet hourglass IP WebFTPMailNewsVideoAudiopingnapster Applications TCP Ethernet 802.11SatelliteOpticalPower linesBluetoothATM Link technologies IP under everything IP on everything

28. January 11, 200527 Network protocols. HTTP TCP IP Files packets Sources Links Files

29. January 11, 200528 Protocol stack Applications TCP IP Hardware Modules packets IP packets Files packets TCP packets packets Layer 2 packets packets Bits

30. January 11, 200529 Animation of the protocols HTTP TCP Files packets TCP packets

31. January 11, 200530 Animation of the protocols HTTP TCP IP Files packets Files packets TCP packets packets TCP packets

32. January 11, 200531 Animation of the protocols HTTP TCP IP Files packets TCP packets packets TCP packets packets IP packets packets IP packets Sources Links packets Layer 2 packets packets Bits

33. January 11, 200532 IP TCP Application TCP Application TCP Application Routing Provisioning Vertical decomposition Protocol Stack Each layer can evolve independently provided: 1.Follow the rules 2.Everyone else does “good enough” with their layer

34. January 11, 200533 IP TCP Application TCP Application TCP Application Routing Provisioning Horizontal decomposition Each level is decentralized and asynchronous

35. January 11, 200534 IP TCP Application TCP Application TCP Application Routing Provisioning Horizontal decomposition Vertical decomposition 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.

36. January 11, 200535 Internet Link IP TCP Application Simplify Device Board Computer Operating System IP TCP Application Interface

37. January 11, 200536 Sources Links

38. January 11, 200537 Hosts Routers packets

39. January 11, 200538 Hosts Routers packets Hidden from the user Files

40. January 11, 200539 Hosts Routers packets

41. January 11, 200540 Hosts Routers packets

42. January 11, 200541 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

43. January 11, 200542 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.

44. January 11, 200543 Overview of the Course WkTue/Thur 1 Introduction to Feedback and Control 2-3System Modeling/Analysis, Review of ODEs, and Laplace Transform 4-5Stability and Performance 6-7Tests for stability 8-9Root locus analysis. Design for time domain specs. 10- 11 Frequency Domain Design: Bode plot. 12- 14 Loop Analysis of Feedback Systems. Nyquist criterion 15Fundamental Limits on Performance 16Uncertainty Analysis and Robustness

45. January 11, 200544 Summary: Introduction to Feedback and Control Sense Compute Actuate Control = Sensing + Computation + Actuation Feedback Principles  Robustness to Uncertainty  Design of Dynamics Many examples of feedback and control in natural & engineered systems: BIO ESE CS

46. January 11, 200545 Summary Feedback control is Every where you just have to look for it

47. January 11, 200546 Welcome to ESE406/505- MEAM513 Control Systems Instructor: Ali Jadbabaie jadbabai@seas.upenn.edu Course website: on Blackboard