Stochastic Modeling and Control
by Ivan Ganchev Ivanov (ed.)
Publisher: InTech 2012
Number of pages: 294
The book provides a self-contained treatment on practical aspects of stochastic modeling and calculus including applications drawn from engineering, statistics, and computer science. Readers should be familiar with basic probability theory and have a working knowledge of stochastic calculus.
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by T.T. Tay, I.M.Y. Mareels, J.B. Moore - Birkhauser
Using the tools of optimal control, robust control and adaptive control, the authors develop the theory of high performance control. Topics include performance enhancement, stabilizing controllers, offline controller design, and dynamical systems.
by Panos J. Antsaklis, Kevin M. Passino (eds) - Kluwer Academic Publishers
An introduction to the field of intelligent control with a broad treatment of topics by several authors (including hierarchical / distributed intelligent control, fuzzy control, expert control, neural networks, planning systems, and applications).
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Several streams of nonlinear control theory are directed towards a constructive solution of the feedback stabilization problem. Analytic, geometric and asymptotic concepts are assembled as design tools for a wide variety of nonlinear phenomena.
by M.R. James - Australian National University
These notes are an overview of some aspects of optimal and robust control theory considered relevant to quantum control. The notes cover classical deterministic optimal control, classical stochastic and robust control, and quantum feedback control.