Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities.
STOCHASTIC PROCESSES, ESTIMATION AND CONTROL.
Finite-dimensional linear estimation; Stochastic processes and linear estimation; Orthogonal increments processes; Estimation in dynamical systems; Linear stochastic control; An outline of further developments.
The book covers both state-space methods and those based on the polynomial approach. Similarities and differences between these approaches are highlighted.
A concise presentation which includes material on the application of time-sampling and spectra to a number of areas. It also includes detailed discussions of both Wierner-Kolmogorov theory and Kalman-Bucy theory....
This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.
A ‘stochastic’ process is a ‘random’ or ‘conjectural’ process, and this book is concerned with applied probability and statistics.
This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand ...
Stochastic Optimal Linear Estimation and Control
This book provides a comprehensive presentation of classical and advanced topics in estimation and control of dynamical systems with an emphasis on stochastic control.
Stochastic Models: Estimation and Control: v. 2