Bøger af Miroslav Krstic

This monograph introduces breakthrough control algorithms for partial differential equation models with moving boundaries, the study of which is known as the Stefan problem.

This concise and highly-usable textbook presents an introduction to backstepping, an elegant new approach to boundary control of partial differential equations (PDEs). Readers are introduced to constructive control synthesis and Lyapunov stability analysis for distributed parameter systems.

Rather than use integrals of the systems vector field, we employ Lie-bracket-based (i.e., derivative-based) averaging. The brief contains numerous examples and applications, including examples with unknown control directions and experiments with charged particle accelerators.

Based on the concept of predictor feedback and infinite-dimensional backstepping transformation for linear systems, the authors guide the reader from the basic ideas of the concept - with constant delays only on the input - all the way through to nonlinear systems with state-dependent delays on the input as well as on system states.

Using a pedagogical style along with detailed proofs and illustrative examples, this book opens a view to the largely unexplored area of nonlinear systems with uncertainties. The focus is on adaptive nonlinear control results introduced with the new recursive design methodology--adaptive backstepping.

This accessible book pioneers feedback concepts for control mixing. It reviews research results appearing over the last decade, and contains control designs for stabilization of channel, pipe and bluff body flows, as well as control designs for the opposite problem of mixing enhancement.

Presents constructive design methods for boundary stabilization and boundary estimation for several classes of benchmark problems in flow control, with potential applications to turbulence control, weather forecasting, and plasma control. This book is suitable for a broad, interdisciplinary engineering and mathematics audience.

This book offers a collection of tools and techniques that make predictor feedback ideas applicable to nonlinear systems, systems modeled by PDEs, and systems with highly uncertain or completely unknown input/output delays.

This book develops stochastic averaging theorems and stochastic extremum-seeking algrithms, illustrating their use in a variety of models. Includes simulation examples based in bacterial locomotion, multi-agent robotic systems, and economic market models.