Difference between revisions of "Architecture and System Design"

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(Created page with "{{Chapter |Chapter number=15 |Previous chapter=Fundamental Limits |Next chapter=Bibliography }}")
 
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|Previous chapter=Fundamental Limits
 
|Previous chapter=Fundamental Limits
 
|Next chapter=Bibliography
 
|Next chapter=Bibliography
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|Chapter summary=# Introduction
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# System and Control Design
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# Top-Down Architectures
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#* Layered Architectures for Control
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#* Online Optimization
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#* Discrete-decision making and supervisory control
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#* Linking Continuous and Discrete Controllers
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#* Model Checking and Program Synthesis
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# Bottom-Up Architectures
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#* Cascade Control -- Several Sensors
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#* Mid-Range Control -- Many Actuators
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#* Selector Control -- Equipment Protection
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\contentsline {subsection
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#* The Smith Predictor -- Phase Advance
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#* Complementary Filtering -- Sensor Fusion
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#* Extremum Seeking or Self-Optimization
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# Interaction
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#* The Relative Gain Array
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#* Parallel Systems
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# Adaptation and Learning
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#* Adaptive Control
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#* Learning
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#* Neural Networks
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#* Deep Learning
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# Control Design in Common Application Fields
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#* Aerospace -- High Performance Systems and Highly Skilled Users
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#* Automotive -- Complex Systems Used by Ordinary People
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#* Process Industry -- Complex Systems with Many Different Users
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#* Telecommunication -- Billions of Systems
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# Further Reading
 
}}
 
}}

Revision as of 00:44, 28 December 2020

Prev: Fundamental Limits Chapter 15 - Architecture and System Design Next: Bibliography

[[Image:{{{Short name}}}-firstpage.png|right|thumb|link=https:www.cds.caltech.edu/~murray/books/AM08/pdf/fbs-{{{Short name}}}_24Jul2020.pdf]]

  1. Introduction
  2. System and Control Design
  3. Top-Down Architectures
    • Layered Architectures for Control
    • Online Optimization
    • Discrete-decision making and supervisory control
    • Linking Continuous and Discrete Controllers
    • Model Checking and Program Synthesis
  4. Bottom-Up Architectures
    • Cascade Control -- Several Sensors
    • Mid-Range Control -- Many Actuators
    • Selector Control -- Equipment Protection

\contentsline {subsection

    • The Smith Predictor -- Phase Advance
    • Complementary Filtering -- Sensor Fusion
    • Extremum Seeking or Self-Optimization
  1. Interaction
    • The Relative Gain Array
    • Parallel Systems
  2. Adaptation and Learning
    • Adaptive Control
    • Learning
    • Neural Networks
    • Deep Learning
  3. Control Design in Common Application Fields
    • Aerospace -- High Performance Systems and Highly Skilled Users
    • Automotive -- Complex Systems Used by Ordinary People
    • Process Industry -- Complex Systems with Many Different Users
    • Telecommunication -- Billions of Systems
  4. Further Reading