Machine Learning Control Taming Nonlinear Dynamics and Turbulence Taming Nonlinear Dynamics and Turbulence
- Engels
- Hardcover
- 9783319406237
- 15 november 2016
- 211 pagina's
Samenvatting
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 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.
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.
Productspecificaties
Inhoud
- Taal
- en
- Bindwijze
- Hardcover
- Oorspronkelijke releasedatum
- 15 november 2016
- Aantal pagina's
- 211
- Illustraties
- Nee
Betrokkenen
- Hoofdauteur
- Thomas Duriez
- Tweede Auteur
- Steven L. Brunton
- Co Auteur
- Bernd R. Noack
- Hoofduitgeverij
- Springer
Overige kenmerken
- Editie
- 1st ed. 2017
- Extra groot lettertype
- Nee
- Product breedte
- 161 mm
- Product hoogte
- 17 mm
- Product lengte
- 241 mm
- Studieboek
- Ja
- Verpakking breedte
- 164 mm
- Verpakking hoogte
- 235 mm
- Verpakking lengte
- 240 mm
- Verpakkingsgewicht
- 279 g
EAN
- EAN
- 9783319406237
Je vindt dit artikel in
- Categorieën
- Beschikbaarheid
- Leverbaar
- Studieboek of algemeen
- Studieboeken
- Taal
- Engels
- Boek, ebook of luisterboek?
- Boek
- Prijs inclusief verzendkosten, verstuurd door bol
- Ophalen bij een bol afhaalpunt mogelijk
- 30 dagen bedenktijd en gratis retourneren
- Dag en nacht klantenservice
Rapporteer dit artikel
Je wilt melding doen van illegale inhoud over dit artikel:
- Ik wil melding doen als klant
- Ik wil melding doen als autoriteit of trusted flagger
- Ik wil melding doen als partner
- Ik wil melding doen als merkhouder
Geen klant, autoriteit, trusted flagger, merkhouder of partner? Gebruik dan onderstaande link om melding te doen.