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The book focuses on the physical and mathematical foundations of
model-based turbulence control: reduced-order modelling and control
design in simulations and experiments. Leading experts provide
elementary self-consistent descriptions of the main methods and
outline the state of the art. Covered areas include optimization
techniques, stability analysis, nonlinear reduced-order modelling,
model-based control design as well as model-free and neural network
approaches. The wake stabilization serves as unifying benchmark
control problem.
The book focuses on the physical and mathematical foundations of
model-based turbulence control: reduced-order modelling and control
design in simulations and experiments. Leading experts provide
elementary self-consistent descriptions of the main methods and
outline the state of the art. Covered areas include optimization
techniques, stability analysis, nonlinear reduced-order modelling,
model-based control design as well as model-free and neural network
approaches. The wake stabilization serves as unifying benchmark
control problem.
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.
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