Mathematical Methods in Robust Control of Discrete-Time Linear Stochastic Systems

Vasile Dragan

ISBN 10: 148998447X / ISBN 13: 9781489984470

Editore: Springer New York, Springer New York, 2014

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Druck auf Anfrage Neuware - Printed after ordering - In this monograph the authors develop a theory for the robust control of discrete-time stochastic systems, subjected to both independent random perturbations and to Markov chains. Such systems are widely used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. The theory is a continuation of the authors' work presented in their previous book entitled 'Mathematical Methods in Robust Control of Linear Stochastic Systems' published by Springer in 2006.Key features:- Provides a common unifying framework for discrete-time stochastic systems corrupted with both independent random perturbations and with Markovian jumps which are usually treated separately in the control literature;- Covers preliminary material on probability theory, independent random variables, conditional expectation and Markov chains;- Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations;- Leads the reader in a natural way to the original results through a systematic presentation;- Presents new theoretical resultswith detailed numerical examples.The monograph is geared to researchers and graduate students in advanced control engineering, applied mathematics, mathematical systems theory and finance. It is also accessible to undergraduate students with a fundamental knowledge in the theory of stochastic systems. Codice articolo 9781489984470

Dati bibliografici

Titolo: Mathematical Methods in Robust Control of ...

Casa editrice: Springer New York, Springer New York

Data di pubblicazione: 2014

Legatura: Taschenbuch

Condizione: Neu

Descrizione articolo

Riassunto:

In this monograph the authors develop a theory for the robust control of discrete-time stochastic systems, subjected to both independent random perturbations and to Markov chains. Such systems are widely used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. The theory is a continuation of the authors’ work presented in their previous book entitled "Mathematical Methods in Robust Control of Linear Stochastic Systems" published by Springer in 2006.

Key features:

- Provides a common unifying framework for discrete-time stochastic systems corrupted with both independent random perturbations and with Markovian jumps which are usually treated separately in the control literature;

- Covers preliminary material on probability theory, independent random variables, conditional expectation and Markov chains;

- Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations;

- Leads the reader in a natural way to the original results through a systematic presentation;

- Presents new theoretical results with detailed numerical examples.

The monograph is geared to researchers and graduate students in advanced control engineering, applied mathematics, mathematical systems theory and finance. It is also accessible to undergraduate students with a fundamental knowledge in the theory of stochastic systems.

Dalla quarta di copertina:

In this monograph the authors develop a theory for the robust control of discrete-time stochastic systems, subjected to both independent random perturbations and to Markov chains. Such systems are widely used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. The theory is a continuation of the authors work presented in their previous book entitled "Mathematical Methods in Robust Control of Linear Stochastic Systems" published by Springer in 2006.

Key features:

- Provides a common unifying framework for discrete-time stochastic systems corrupted with both independent random perturbations and with Markovian jumps which are usually treated separately in the control literature

- Covers preliminary material on probability theory, independent random variables, conditional expectation and Markov chains

- Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations

- Leads the reader in a natural way to the original results through a systematic presentation

- Presents new theoretical results with detailed numerical examples

 

The monograph is geared to researchers and graduate students in advanced control engineering, applied mathematics, mathematical systems theory and finance. It is also accessible to undergraduate students with a fundamental knowledge in the theory of stochastic systems.