Robust Estimators for Systems with Deterministic and Stochastic
Uncertainties
Robust Estimators for Systems with Deterministic and Stochastic
Uncertainties
F. Wang and V. Balakrishnan
In Proc. IEEE Conference on Decision and Control,
pages 1946-1951, Phoenix, Arizona, December 1999
Abstract:
For uncertain systems containing both deterministic and
stochastic uncertainties, we consider two problems of
optimal estimation. The first is the design of a filter
that minimizes an upper bound on the worst-case gain in the
mean energy between the noise affecting the system and the
estimation error. The second is the design of a filter that
minimizes an upper bound on the worst-case asymptotic mean
square estimation error when the plant is
driven by a white noise process. We present filtering
algorithms that solve each of these problems, with the
filter parameters determined via convex
optimization based on linear matrix inequalities. We
demonstrate the performance of these robust algorithms on a
numerical example consisting of the design of equalizers for
a communication channel.
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