Description
Product ID: | 9783319406237 |
Product Form: | Hardback |
Country of Manufacture: | CH |
Series: | Fluid Mechanics and Its Applications |
Title: | Machine Learning Control – Taming Nonlinear Dynamics and Turbulence |
Authors: | Author: Bernd R. Noack, Thomas Duriez, Steven L. Brunton |
Page Count: | 211 |
Subjects: | Physics: Fluid mechanics, Fluid mechanics, Engineering: Mechanics of fluids, Automatic control engineering, Algorithms and data structures, Mechanics of fluids, Automatic control engineering, Algorithms & data structures |
Description: | Select Guide Rating This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. 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 low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers 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. |
Imprint Name: | Springer International Publishing AG |
Publisher Name: | Springer International Publishing AG |
Country of Publication: | GB |
Publishing Date: | 2016-11-15 |