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This book contains a collection of survey papers in the areas of modelling, estimation and adaptive control of stochastic systems describing recent efforts to develop a systematic and elegant theory of identification and adaptive control. It is meant to provide a fast introduction to some of the recent achievements. The book is intended for graduate students and researchers interested in statistical problems of control in general. Students in robotics and communication will also find it valuable. Readers are expected to be familiar with the fundamentals of probability theory and stochastic processes.
List of contents
Direct modeling of white noise in stochastic systems.- Markovian representations of cyclostationary processes.- Parametriztions of linear stochastic systems.- Stochastic realization for approximate modeling.- Representation of inner products and stochastic realization.- On realization and identification of stochastic bilinear systems.- On stochastic partial differential equations. Results on approximations.- Developments in parameter bounding.- Recent progress in parallel stochastic approximations.- On the adaptive stabilization and ergodic behaviour of stochastic systems with jump-Markov parameters via nonlinear filtering.- Identification and adaptive control for ARMAX systems.- Some methods for the adaptive control of continuous time linear stochastic systems.- Strong approximation results in estimation and adaptive control.- Stochastic adaptive control: Results and perspective.- Information bounds, certainty equivalence and learning in asymptotically efficient adaptive control of time-invariant stochastic systems.- Stability of Markov chains on topological spaces with applications to adaptive control and time series analysis.
