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8/12/2019 The Future of Control (Astrom)
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Some personal reflections
The Future of Control
K. J. strmDepartment of Automatic Control LTH
Lund University
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
A Broad PictureThere are examples of control from ancient time but
control became widely used in the industries that emergedin the 19th and 20th centuries: steam power, electric power,
ships, aircrafts, chemicals, telecommunication. Control
was sometimes an enabling technology (aircraft, telecom).
Similarities between different disciplines were notrecognized.
Control became a separate engineering discipline in the
1940s and it has developed rapidly ever since. Todaythere are applications everywhere and the field faces new
challenges
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
TheThe HiddenHidden TechnologyTechnology
Widely used
Very successful
Seldom talked about
Except when there is a disaster Why?
Easier to talk about devices than ideas.
We have not presented our ideas well tocollegues in science and to broader audiences
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
1. Introduction
2. A Brief History
3. Control Everywhere
4. Challenges
5. Conclusions
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Early use in many fieldsPower systems
Process control
Vehicle control
Communication Servomechanism Theory
Consequences
The Second Phase The Third Phase?
A Brief History
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Power Generation
Problem: Generate AC atconstant frequency
Solution: Turbincontroller (PI)
Side effects: Routh Hurwitzstability theory (Maxwell and
Stodola)
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Flight Control
Problem: How to fly? Solution: Understand dynamics.
Build maneuverable but unstable
aircraft stabilize with manual
control
Side effect: Autopilots
Sperry 1913
Autonomy 1947
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Telecommunication Problem: How to phone over long distances? How to make a
good amplifier from bad components (vacuum tubes)
Solution: The feedback amplifier
Side effects: Stability and design theory (Nyquist, Bode)
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Accurate systems from imprecise components
Reduce effects of disturbances and component variations
Regulate, stabilize, and shape behavior
Drawbacks:
Risk of Instability
Sensor noise is fed into the system
The Power of Feedback
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
The Scene of 1940
Widespread use of control in many fields
Power generation and distribution
Process control
Autopilots for ships and aircrafts
Telecommunications
The similarities were not recognized
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
The Discipline Emerges
Drivers: The war effort, gun sights, radar,
Concepts: Feedback, feedforward
Design tools: Block diagrams, transfer functions Simulation: Analog
Implementation: Analog computing
Holistic view of theory and applications
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
ServomechanismsServomechanisms
Theory
Complex variables
Laplace Transforms
System ConceptsFeedback
Feedforward
Design
Frequency Response
Graphical Methods
Analog simulation
Implementation
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
TheoryTheory ofof ServomechanismsServomechanisms
Hubert M. James
Professor of Physics Purdue University
Nathaniel B. Nichols
Director of Research Taylor Instrument Companies
Ralph S. Phillips
Associate Professor of Mathematics University of Southern California
Office of Scientific Research and Development
National Defence Research Committee
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
ConsequencesConsequences
Education
ApplicationIndustrialization
Organisation
JournalsConferences
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Optimal ControlOptimal Control
Hamilton, Jacobi, Bellman 1957
Euler, Lagrange, Pontryagin 1962
Model predictive control
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Kalman 1961:
Efficient way to filter signals
Combine measurements and mathematical modelto estimate process state
New controller structure based on Kalman filter
(Observer), state feedback (L) and feedforward
generator
KalmanKalman FilteringFiltering
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Robust Control
Classic Bode: non-minimum phase is important
State space: reachability and observabilty
Robustness of state feedback
Non-robustness of output feedback Robust Control:Youla, Zames, 4 author paper:
Doyle, Glover, Khargonekar, Francis
Fundamental limitations
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
1. Introduction
2. A Brief History
3. Control Everywhere
4. Challenges
5. Conclusions
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Keep graphics
below the te and
to the right
Power Generation andDistribution
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Process Control
Keep graphics
below the te and
to the right
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Buildings
Design &Energy Analysis
Windows &Lighting
HVAC
Safety
Elevators
IndoorEnvironment
NaturalVentilation
Vibrationdamping
Sensors, Networks,Communications, ControlsSlide from UTRC
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Vehicles
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Consumer Electronics
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PhysicsPhysics
The causality issue Nobel prizes in Physics
Gustaf Dalen 1912
Simon van der Meer 1984
Quantum and molecular systems
Turbulence
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Instruments Giga to NanoAdaptive Optics Atomic Force Microscope
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Feedback is a central feature of life. The process of
feedback governs how we grow, respond to stress
and challenge, and regulate factors such as body
temperature, blood pressure, and cholesterol level.The mechanisms operate at every level, from the
interaction of proteins in cells to the interaction of
organisms in complex ecologies.
Mahlon B Hoagland and B Dodson The Way Life Works Times Books 1995
BiologyBiology
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ChallengesChallengesIncreased use in engineeringIncreased use in engineering
NetworksNetworks
Autonomous systemsAutonomous systems
Learning, reasoning and cognitionLearning, reasoning and cognition
Natural scienceNatural science
Devices and ideas in physicsDevices and ideas in physics
Strong systems orientation in biologyStrong systems orientation in biology
Many previous attempts. Will it work this time?Many previous attempts. Will it work this time?
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
New ProblemsNew Problems Complex networked systems
Sensor rich control
Actuator rich control
High level control principles Safe design of embedded systems
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Autonomous Systems Adaptation
Learning Cognition
Safety Diagnostics
Maintenance
Reconfiguration
Dickmanns 1995 - 95% autonomi
Darpa Grand Challenge
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
The Holistic View
Process and control co-design (architecture)
Modeling (physics and data)
Architecture (sensors, actuators, MMI)
Control design (remember simple controllers) Implementation (hardware, software)
Verification
Commissioning (tuning) Operation (diagnostics, prognostics)
Upgrade and reconfiguration
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AFOSRNAE
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Control Computing
Logic, languages, DES,FSM
High complexity, abstractions
Architecture
The CS Barrier
Feedback,Stability, ODE, PDEModerate complexity
Robustness
The controller
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Modeling and Simulation
There will be growth in areas of simulationand modeling around the creation of new
engineering structures. Computer-based
design-build engineering ... will become the
norm for most product designs,
accelerating the creation of complex
structures for which multiple subsystems
combine to form a final product.
NAE The Engineer of 2020
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Automotive Climate Control
Picture courtesy of Behr GmbH & Co.
Audi, BMW, DaimlerCrysler,
Volkswagen and their suppliers havestandardized on Modelica
Suppliers provide components and
validated Modelica models based on
the AirConditioning library fromModelon
Car manufacturers evaluate
complete system by simulation
IP protected by extensive encryption
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Educational ChallengesEducational Challenges Educating the future engineers
Education of physicists and biologist
Dilemma of emerging fields
Filter out the fundamentals and exploit
advances in computation
Deep knowledge in specific areas
Broad knowledge of neighboring fields
Ability to communicate and to work in
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Trademark Automatic Control
Systems and Control
Control and Automation
Control and Dynamical Systems
Cyberphysical Systems
Cybernetics
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
1. Introduction
2. A Brief History
3. Control Everywhere4. Challenges
5. Conclusions
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Lund University / LTH/Automatic Control IEEE Berchtesgaden
Conclusions Control is a vital dynamic field
Networked embedded systems
Autonomy and safety
The educational challenge
Recover the holistic view
Our trademark
CM P
C
C