This paper investigates the problem of H , estimation of nonlinear processes. An estimator, which may be nonlinear, is looked for so that a given bound on the ratio between the energy of the estimation error and the energy of the exogeneous inputs to the estimated process is achieved. Conditions for
On regional nonlinear H∞-filtering
✍ Scribed by Emilia Fridman; Uri Shaked
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 357 KB
- Volume
- 29
- Category
- Article
- ISSN
- 0167-6911
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✦ Synopsis
The structure of the nonlinear H~-filter in the neighborhood of the estimated trajectory is investigated and a bound on the size of the neighbc.rhood that allows this structure is determined, both for finite and infinite horizons. Riccati inequalities that depend on the estimated trajectory are derived for finding the filter gain matrix and an algorithm for calculating the bound on the size of the above neighborhood is presented. Explicit formulas are obtained in the infinite horizon case for the minimum achievable disturbance attenuation level, the size of the neighborhood, and the corresponding filter gain.
📜 SIMILAR VOLUMES
A robust (or H∞) approach to ÿltering for nonlinear systems is considered. A bound on the estimate error as a function of the disturbance energy is obtained. The corresponding dynamic programming equation is a ÿrst-order PDE. This has computational ramiÿcations. The case where the measurements are d
This paper considers the problem of sequential estimation of the state of a nonlinear process from noisy measurement data. In a previous paper [1] the problem was concerned with measurements which are linear combinations of the state variables. In this paper a different approach to the problem is us
Now suppose that a filter (1.3) exists. We can assume that (1.3) is minimal. Then there are 2 ways of calculating y(x,), viz. via (1.3) and also via (1.2) because given p(x,t), y(x,) can be obtained by first normalizing and then integrating y(x,) against the normalized conditional density.