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Machine Learning Control (MLC)

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

Abstract

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.

Keywords

Genetic Algorithm Cost Function Evolutionary Algorithm Genetic Programming Support Vector Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Thomas Duriez
    • 1
  • 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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