2017  Buch
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence
Über dieses Buch
This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a lowdimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shearlayer to turbulent boundarylayers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.
 Titel
 Machine Learning Control – Taming Nonlinear Dynamics and Turbulence
 Print ISBN
 9783319406237
 Electronic ISBN
 9783319406244
 CopyrightJahr
 2017
 DOI

https://doi.org/10.1007/9783319406244
 Autoren:

Thomas Duriez
Steven L. Brunton
Bernd R. Noack
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