Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

• Κύριοι συγγραφείς: Duriez, Thomas (Συγγραφέας), Brunton, Steven L. (Συγγραφέας), Noack, Bernd R. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Fluid Mechanics and Its Applications, 116
• Θέματα: Engineering.; Microprogramming.; Artificial intelligence.; Fluids.; Fluid mechanics.; Control engineering.; Engineering Fluid Dynamics.; Fluid- and Aerodynamics.; Control.; Control Structures and Microprogramming.; Artificial Intelligence (incl. Robotics).; Applications of Nonlinear Dynamics and Chaos Theory.

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...

Πίνακας περιεχομένων:

• 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. .