國立虎尾科技大學 |

Machine learning control - taming nonlinear dynamics and turbulence

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning control - taming nonlinear dynamics and turbulence/ by Thomas Duriez, Steven L. Brunton, Bernd R. Noack.
Author:	Duriez, Thomas.
other author:	Brunton, Steven L.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xx, 211 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Feedback control systems. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-40624-4
ISBN:	9783319406244

Machine learning control - taming nonlinear dynamics and turbulence
Duriez, Thomas.

Machine learning control - taming nonlinear dynamics and turbulence[electronic resource] /by Thomas Duriez, Steven L. Brunton, Bernd R. Noack. - Cham :Springer International Publishing :2017. - xx, 211 p. :ill. (some col.), digital ;24 cm. - Fluid mechanics and its applications,v.1160926-5112 ;. - Fluid mechanics and its applications ;v. 87. .

Preface -- 1 Introduction -- 1.1 Feedback in engineering and living systems -- 1.2 Benefits of feedback control -- 1.3 Challenges of feedback control -- 1.4 Feedback turbulence control is a grand challenge problem -- 1.5 Nature teaches us the control design -- 1.6 Outline of the book -- 1.7 Exercises -- 2 Machine learning control (MLC) -- 2.1 Methods of machine learning -- 2.2 MLC with genetic programming -- 2.3 Examples -- 2.4 Exercises -- 2.5 Suggested reading -- 2.6 Interview with Professor Marc Schoenauer -- 3 Methods of linear control theory -- 3.1 Linear systems -- 3.2 Full-state feedback -- Linear quadratic regulator (LQR) -- 3.3 Sensor-based state estimation -- Kalman filtering -- 3.4 Sensor-based feedback -- Linear quadratic Gaussian (LQG) -- 3.5 System Identification and Model Reduction -- 3.6 Exercises -- 3.7 Suggested reading -- 4 Benchmarking MLC against linear control -- 4.1 Comparison of MLC with LQR on a linear oscillator -- 4.2 Comparison of MLC with Kalman filter on a noisy linear oscillator -- 4.3 Comparison of MLC with LQG for sensor-based feedback -- 4.4 Modifications for small nonlinearity -- 4.5 Exercises -- 4.6 Interview with Professor Shervin Bagheri -- 5 Taming nonlinear dynamics with MLC -- 5.1 Generalized mean-field system -- 5.2 Machine learning control -- 5.3 Derivation outline for the generalized mean-field model -- 5.4 Alternative control approaches -- 5.5 Exercises -- 5.6 Suggested reading -- 5.7 Interview with Professor Mark N. Glauser -- 6 Taming real world flow control experiments with MLC -- 6.1 Separation control over a backward-facing step -- 6.2 Separation control of turbulent boundary layers -- 6.3 Control of mixing layer growth -- 6.4 Alternative model-based control approaches -- 6.5 Implementation of MLC in experiments -- 6.6 Suggested reading -- 6.7 Interview with Professor David Williams -- 7 MLC tactics and strategy -- 7.1 The ideal flow control experiment -- 7.2 Desiderata of the control problem -- from the definition to hardware choices -- 7.3 Time scales of MLC -- 7.4 MLC parameters and convergence -- 7.5 The imperfect experiment -- 8 Future developments -- 8.1 Methodological advances of MLC -- 8.2 System-reduction techniques for MLC -- Coping with high-dimensional input and output -- 8.3 Future applications of MLC -- 8.4 Exercises -- 8.5 Interview with Professor Belinda Batten -- Glossary -- Symbols -- Abbreviations -- Matlab® Code: OpenMLC -- Bibliography -- Index.

This is the first book 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.

ISBN: 9783319406244

Standard No.: 10.1007/978-3-319-40624-4doiSubjects--Topical Terms:

596555
Feedback control systems.

LC Class. No.: TJ216

Dewey Class. No.: 629.83

Machine learning control - taming nonlinear dynamics and turbulence
LDR:04828nam a2200349 a 4500 001 957218
003 DE-He213
005 20170609085525.0
006 m d
007 cr nn 008maaau
008 201118s2017 gw s 0 eng d
020 $a 9783319406244 $q (electronic bk.)
020 $a 9783319406237 $q (paper)
024 7 $a 10.1007/978-3-319-40624-4 $2 doi
035 $a 978-3-319-40624-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TJ216
072 7 $a TGMF $2 bicssc
072 7 $a TGMF1 $2 bicssc
072 7 $a TEC009070 $2 bisacsh
072 7 $a SCI085000 $2 bisacsh
082 0 4 $a 629.83 $2 23
090 $a TJ216 $b .D962 2017
100 1 $a Duriez, Thomas. $3 1248715
245 1 0 $a Machine learning control - taming nonlinear dynamics and turbulence $h [electronic resource] / $c by Thomas Duriez, Steven L. Brunton, Bernd R. Noack.
260 $a Cham : $c 2017. $b Springer International Publishing : $b Imprint: Springer,
300 $a xx, 211 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Fluid mechanics and its applications, $x 0926-5112 ; $v v.116
505 0 $a Preface -- 1 Introduction -- 1.1 Feedback in engineering and living systems -- 1.2 Benefits of feedback control -- 1.3 Challenges of feedback control -- 1.4 Feedback turbulence control is a grand challenge problem -- 1.5 Nature teaches us the control design -- 1.6 Outline of the book -- 1.7 Exercises -- 2 Machine learning control (MLC) -- 2.1 Methods of machine learning -- 2.2 MLC with genetic programming -- 2.3 Examples -- 2.4 Exercises -- 2.5 Suggested reading -- 2.6 Interview with Professor Marc Schoenauer -- 3 Methods of linear control theory -- 3.1 Linear systems -- 3.2 Full-state feedback -- Linear quadratic regulator (LQR) -- 3.3 Sensor-based state estimation -- Kalman filtering -- 3.4 Sensor-based feedback -- Linear quadratic Gaussian (LQG) -- 3.5 System Identification and Model Reduction -- 3.6 Exercises -- 3.7 Suggested reading -- 4 Benchmarking MLC against linear control -- 4.1 Comparison of MLC with LQR on a linear oscillator -- 4.2 Comparison of MLC with Kalman filter on a noisy linear oscillator -- 4.3 Comparison of MLC with LQG for sensor-based feedback -- 4.4 Modifications for small nonlinearity -- 4.5 Exercises -- 4.6 Interview with Professor Shervin Bagheri -- 5 Taming nonlinear dynamics with MLC -- 5.1 Generalized mean-field system -- 5.2 Machine learning control -- 5.3 Derivation outline for the generalized mean-field model -- 5.4 Alternative control approaches -- 5.5 Exercises -- 5.6 Suggested reading -- 5.7 Interview with Professor Mark N. Glauser -- 6 Taming real world flow control experiments with MLC -- 6.1 Separation control over a backward-facing step -- 6.2 Separation control of turbulent boundary layers -- 6.3 Control of mixing layer growth -- 6.4 Alternative model-based control approaches -- 6.5 Implementation of MLC in experiments -- 6.6 Suggested reading -- 6.7 Interview with Professor David Williams -- 7 MLC tactics and strategy -- 7.1 The ideal flow control experiment -- 7.2 Desiderata of the control problem -- from the definition to hardware choices -- 7.3 Time scales of MLC -- 7.4 MLC parameters and convergence -- 7.5 The imperfect experiment -- 8 Future developments -- 8.1 Methodological advances of MLC -- 8.2 System-reduction techniques for MLC -- Coping with high-dimensional input and output -- 8.3 Future applications of MLC -- 8.4 Exercises -- 8.5 Interview with Professor Belinda Batten -- Glossary -- Symbols -- Abbreviations -- Matlab® Code: OpenMLC -- Bibliography -- Index.
520 $a This is the first book 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.
650 0 $a Feedback control systems. $3 596555
650 0 $a Adaptive control systems. $3 528425
650 0 $a Machine learning. $3 561253
650 1 4 $a Engineering. $3 561152
650 2 4 $a Engineering Fluid Dynamics. $3 670525
650 2 4 $a Fluid- and Aerodynamics. $3 768779
650 2 4 $a Control. $3 782232
650 2 4 $a Control Structures and Microprogramming. $3 669788
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Applications of Nonlinear Dynamics and Chaos Theory. $3 1113607
700 1 $a Brunton, Steven L. $3 845565
700 1 $a Noack, Bernd R. $3 786545
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Fluid mechanics and its applications ; $v v. 87. $3 773513
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-40624-4
950 $a Engineering (Springer-11647)