東華大學圖書館 |

Machine learning control - taming nonlinear dynamics and turbulence

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning control - taming nonlinear dynamics and turbulence/ by Thomas Duriez, Steven L. Brunton, Bernd R. Noack.
作者:	Duriez, Thomas.
其他作者:	Brunton, Steven L.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xx, 211 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Feedback control systems. -
電子資源:	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.116..

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:

531860
Feedback control systems.

LC Class. No.: TJ216

Dewey Class. No.: 629.83

Machine learning control - taming nonlinear dynamics and turbulence
LDR:04828nmm a2200349 a 4500 001 2088527
003 DE-He213
005 20170609085525.0
006 m d
007 cr nn 008maaau
008 171013s2017 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 3218360
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 : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
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 531860
650 0 $a Adaptive control systems. $3 533905
650 0 $a Machine learning. $3 533906
650 1 4 $a Engineering. $3 586835
650 2 4 $a Engineering Fluid Dynamics. $3 891349
650 2 4 $a Fluid- and Aerodynamics. $3 1066670
650 2 4 $a Control. $3 1006321
650 2 4 $a Control Structures and Microprogramming. $3 895886
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Applications of Nonlinear Dynamics and Chaos Theory. $3 3134772
700 1 $a Brunton, Steven L. $3 3218361
700 1 $a Noack, Bernd R. $3 3218362
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Fluid mechanics and its applications ; $v v.116. $3 3218363
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-40624-4
950 $a Engineering (Springer-11647)