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Mem 255 Lecture 1 was an English blacksmith, who Prepared by Harry Kwatny and B. C. Chang invented the atmospheric steam , an improvement over Thomas Savery's previous design. 1 A Brief History of Control Engineering The Newcomen engine was the predecessor to the engine and it was one of the most interesting pieces of technology developed during the 1700's. 1712

James Watt (1736-1819) was a Scottish inventor and mechanical engineer, born in , who was renowned for his improvements of the . Watt's engine soon became the dominant design for all modern steam and helped bring about the .

James Watt designed his first governor in 1788 following a 2 Water Clock 270 B.C. suggestion from his business partner . It A Water Clock, or clepsydra, (a Greek word meaning water was a conical pendulum governor (flyball governor) and thief) is a device that uses the flow of water under gravity one of the final series of innovations Watt had employed to measure time. for steam engines. 3 Ktesibios (300-230 B.C.) used a float in the overflow tank James Clerk : Governors 1868 to regulate the level, and therefore, the flow. wrote a famous paper "On governors", Ktesibios is considered the first builder of a system with which is quite frequently considered a classical paper in control. feedback control theory, in 1868.

Routh and Hurwitz 1877, 1895 Windmills 1787 Edward Routh: Stability of motion, Steam Engines 1788 Adolf Hurwitz: Routh-Hurwitz Criterion Thomas Savery was an English military engineer and Wright brothers: Airplane 1901 inventor who in 1698, patented the first crude steam engine, based on Denis Papin's Digester or pressure cooker Wilber Wright in 1901, ”We know how to construct of 1679. airplanes. Men also know how to build engines. The inability to balance and steer still confronts students of the 1 S. Bennett, “A brief history of automatic control,” IEEE Control Systems Magazine, June, flying problem. When this one feature has been worked 1996, pp.17-25. 2 J. Goodenow, et. al., “Mathematical models of water clocks,” Rochester Institude of Technology, Rochester, NY, 14623. http://www.uni- 3 J.C. Maxwell, “On governors,” Proceedings of the Royal Society of , 1868, pp. 270- graz.at/exp2www/Physikgeschichte/Water%20Clocks.pdf 283.

3 4 out, the age of flying will have arrived, for all other difficulties are of minor importance.” What is Control? Elmer Sperry: 1914 • Manipulation of the inputs to a physical system in order to cause desirable behavior. Automatic ship steering mechanism, PID control and automatic gain adjustment to compensate for the Cause output variables to track desired values disturbances Impose desirable dynamical behavior, e.g., stabilize Nicholas Minorsky: 1922 an unstable system Steering control of ship, • Open loop (feedforward) control Position control system analysis, PID controller Exploit knowledge of system behavior to compute Harold Hazen: 1934 necessary inputs Requires accurate model of system Servomechanism theory • Closed loop (feedback, active) control Harry Nyquist: 1932 Process information from sensors to derive Nyquist Analysis appropriate inputs Harold Black: 1934 Allows compensation for model uncertainty, amplifier disturbances, noise Negative feedback on systems Alters system dynamics A. Ivanoff : 1933

Temperature regulation R ()s U ()s Y()s K()s Gs()

R ()s + E()s U ()s Y()s K()s Gs() − H ()s

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Contemporary Applications

The Magic of Feedback Widespread use of automatic control in many fields • The adjustment of system inputs based on the observation of its outputs • generation • Power transmission • Feedback is a universal strategy to cope with uncertainty • Process control • Discrete manufacturing In engineering we use feedback: • Robotics • Cause a system to behave as desired • Communications • Keep variables constant • Automotive • Stabilize unstable system • Buildings • Reduce effects of disturbances • Aerospace • Medicine • Minimize the effect of component variations • Marine Engineering • Computers • Instrumentation • Mechatronics • Materials • Physics • Biology • Economics There is a unified framework of theory, design methods and computer tools that cut across fields of application.

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Examples Vapor deposition

• Noise and vibration control • Flight control systems Active mounts Commercial & military “fly-by-wire” Speaker systems Autopilot, auto-landing • Intelligent vehicle highway systems UAV ‘platooning’ for high speed, high density travel • Robotics Automatic merge Precision positioning in manufacturing Obstacle avoidance Remote space/sea environments • Smart engines Minimally-invasive surgery Compression systems stall, surge, flutter RPV’sfor surveillance, search and rescue control • Automotive Combustion systems lean air/fuel ratio for low Engine emissions, improved efficiency Transmission Cruise, climate control ABS, Traction control, ESP Active suspension • Power plants Various temps/pressures Power output Emissions control • Heating, ventilation, air conditioning (HVAC) • Materials processing Rapid thermal processing

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Aerospace Control Telecommunications: The Feedback Amplifier The Wright Brothers 1903 The Goal: Telephone Calls Over Long Distances Sperry’s Autopilot 1912 The Problem: How to Increase Signal Strength? V1 & V2 Rockets 1942 The Solution: The Feedback Amplifier Sputnik 1957 application by Black 1928 Patent Granted 1937 Apollo 1969 Development of the feedback amplifier led to Mars Pathfinder 1997 new approaches to stability analysis and new V-22 Osprey 2000 methods of feedback system design. International Space Station

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Automotive Control Systems Biology/Biomechanics  • Cruise Control • Feedback governs how we grow, respond to • ABC-active body control stress and challenge. • ABS-anti-lock braking system • Feedback regulates factors such as body • ASR acceleration skid control temperature, blood pressure, and cholesterol level. • ESP electronic stabilization program • Feedback makes it possible for us to stand • SBC sensotronic brake control upright. • BAS brake assist system • Feedback enables locomotion. • Proximity controlled cruising • Feedback operates at every level, from the A typical automobile has 200-300 feedback interaction of proteins in cells to the interaction controllers. Here are a few examples in a of organisms in complex ecologies. Feedback contemporary Mercedes. control is used to design drug treatment strategies for diseases like HIV/Aids, Cancer • Biologically inspired control

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Evolution of the Control Discipline • Key Technology Trends Classical control 1940 Frequency-domain based tools for linear • Computation/microprocessors systems Mainly useful for single-input single-output Cheap and powerful microprocessors opened (SISO) systems the door to widespread control applications WWII years saw 1stapplication of from 1970’s onward ‘optimal’control • Sensors and actuators Still the main tools used in practice Sensors continue to get smaller, cheaper, faster • Modern control 1960 Macro/micro –scale actuation evolving (power ‘State space’approach for linear systems electronics, piezo-electric, EM-rheological fluids) Useful for SISO and multi-input multi-output

(MIMO) systems • Communications and networking Relies on linear algebra computations rather Networks replacing point-to-point than Laplace transform communication in large systems (e.g., electric Performance and robustness measures not power systems) and small (e.g. automotive) always explicit Just in time for space exploration • Optimal control 1970 Find the input that optimizes some objective function (e.g., minfuel, min time) Used for both open loop and closed loop design • Robust control 1980 Generalizes classical control to MIMO case Enabled by modern control development • Nonlinear, adaptive, hybrid … 2000

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Research Applications in ME • Automotive • Aircraft/Flight Safety • Power Plants

• Robotics • Autonomous Vehicles Lab Video Demo • Mechatronics • Biology/Biomechanics • Electric Power Systems

Summary • Course content. • What is a control system? Open loop/closed loop (feedforward/feedback) • Why is control relevant to ME? Applications! Applications! Applications! • Why so much math? Abstraction to accommodate many applications in a common framework Explicit design approaches to meet (optimize) specific performance goals.