Abstract
Many dynamical systems involve not only process and measurement noise signals but also parameter uncertainty and known input signals. When /spl Lscr//sub 2/ or /spl Hscr//sub /spl infin// filters that were designed based on a "nominal" model of the system are applied, the presence of parameter uncertainty will not only affect the noise attenuation property of the filter but also introduce a bias proportional to the known input signal, and the latter may be very appreciable. We introduce a finite-horizon robust /spl Hscr//sub /spl infin// filtering method that provides a guaranteed /spl Hscr//sub /spl infin// bound for the estimation error in the presence of both parameter uncertainty and a known input signal. This method is developed by using a game-theoretic approach, and the results generalize those obtained for cases without parameter uncertainty or without a known input signal. It is also demonstrated, via an example, that the proposed method provides significantly improved signal estimates.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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