Machine Learning Control (MLC)

  • Thomas DuriezEmail author
  • Steven L. Brunton
  • Bernd R. Noack
Part of the Fluid Mechanics and Its Applications book series (FMIA, volume 116)


This chapter discusses the central topic of this book: the use of powerful techniques from machine learning to discover effective control laws for complex, nonlinear dynamics. The machine learning control (MLC) framework is then developed using genetic programming as a search algorithm to find control laws that are not accessible through linear control theory. Implementation details and example codes are also provided.


Genetic Algorithm Cost Function Evolutionary Algorithm Genetic Programming Support Vector Regression 
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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Thomas Duriez
    • 1
    Email author
  • Steven L. Brunton
    • 2
  • Bernd R. Noack
    • 3
    • 4
  1. 1.Laboratorio de Fluido DinámicaCONICET - Universidad de Buenos AiresBuenos AiresArgentina
  2. 2.Mechanical Engineering DepartmentUniversity of WashingtonSeattleUSA
  3. 3.Département Mécanique-EnergétiqueLIMSI-CNRS, UPR 3251OrsayFrance
  4. 4.Institut für StrömungsmechanikTechnische Universität BraunschweigBraunschweigGermany

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