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Can I read Filtering and Control Performance Bounds with Implications on Asymptotic Separation on EtoBox?
Filtering and Control Performance Bounds with Implications on Asymptotic Separation by Donald L. Snyder; Ian B. Rhodes is a Engineering article available to read on EtoBox.
What is Filtering and Control Performance Bounds with Implications on Asymptotic Separation about?
Lower bounds can be derived on the error covariance in causally filtering from various nonlinear observation formats, including doubly-stochastic Poisson processes. These lead to performance bounds and conditions for asymptotic separation in stochastic control problems. Summary--A bound is derived on the accuracy in causally estimating a Gaussian process from nonlinear observations. Both additive Gaussian noise and Poisson observations are included. The bound is used to study the control of a stochastic linear dynamical system with nonlinear observations of either type and an average quadratic cost. An asymptotic Separation Theorem is established showing that a linear feedback control law, involving a state estimate, is asymptotically optimum as the accuracy of the state estimate approaches the bound.
Who reads Filtering and Control Performance Bounds with Implications on Asymptotic Separation?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Donald L. Snyder; Ian B. Rhodes
- Publisher
- Elsevier Science; Elsevier ; Elsevier Ltd.; Test accounts; Elsevier BV (ISSN 0005-1098)
- Published
- 1972
- Language
- EN
- Field
- Engineering (Physical Sciences)