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Control and Dynamic Systems: Advances in Theory and Applications reviews progress in the field of control and dynamic systems theory and applications, with emphasis on filtering and stochastic control in dynamic systems. Topics include linear and nonlinear filtering techniques; concepts and methods in stochastic control; and discrete-time optical stochastic observers. The theory of disturbance-accommodating controllers is also presented.
Comprised of nine chapters, this volume begins with an overview of filtering and stochastic control in dynamic systems, followed by a discussion on linear and nonlinear filtering techniques. The reader is then introduced to concepts and methods in stochastic control, as well as the innovations process and its applications to sensitivity analysis and system identification. Subsequent chapters focus on the status of observer theory and its major results as applied to discrete-time linear systems; the properties of the class of discrete-time Riccati equations that arise in the filtering problem; and the theory of disturbance-accommodating controllers. The identification of noise characteristics in a Kalman filter and estimation of adaptive minimum variance in discrete-time linear systems round out the book.
This monograph will be useful to practicing technologists and research workers interested in filtering and stochastic control in dynamic systems.
Comprised of nine chapters, this volume begins with an overview of filtering and stochastic control in dynamic systems, followed by a discussion on linear and nonlinear filtering techniques. The reader is then introduced to concepts and methods in stochastic control, as well as the innovations process and its applications to sensitivity analysis and system identification. Subsequent chapters focus on the status of observer theory and its major results as applied to discrete-time linear systems; the properties of the class of discrete-time Riccati equations that arise in the filtering problem; and the theory of disturbance-accommodating controllers. The identification of noise characteristics in a Kalman filter and estimation of adaptive minimum variance in discrete-time linear systems round out the book.
This monograph will be useful to practicing technologists and research workers interested in filtering and stochastic control in dynamic systems.
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