Abstract
The problem of H∞ filtering of stationary discrete-time linear systems with stochastic
uncertainties in the state space matrices is addressed, where the uncertainties are modeled
as white noise. The relevant cost function is the expected value of the standard H∞ performance index with respect to the uncertain parameters. A previously developed stochastic
bounded real lemma is applied which results in a modified Riccati inequality. This inequality
is expressed in a linear matrix inequality form whose solution provides the filter parameters.
The method proposed is applied also to the case where, in addition to the stochastic uncertainty, other deterministic parameters of the system are not perfectly known and are assumed
to lie in a given polytope. The problem of mixed H2/H∞ filtering for the above system is
also treated. The theory developed is demonstrated by a simple tracking example.
uncertainties in the state space matrices is addressed, where the uncertainties are modeled
as white noise. The relevant cost function is the expected value of the standard H∞ performance index with respect to the uncertain parameters. A previously developed stochastic
bounded real lemma is applied which results in a modified Riccati inequality. This inequality
is expressed in a linear matrix inequality form whose solution provides the filter parameters.
The method proposed is applied also to the case where, in addition to the stochastic uncertainty, other deterministic parameters of the system are not perfectly known and are assumed
to lie in a given polytope. The problem of mixed H2/H∞ filtering for the above system is
also treated. The theory developed is demonstrated by a simple tracking example.
| Original language | American English |
|---|---|
| Title of host publication | Proceedings of the 7th Mediterranean Conference on Control and Automation (MED99) |
| Pages | 1299-1310 |
| Number of pages | 11 |
| State | Published - 1999 |