Abstract
We address the robust filtering problem for a wide class of systems whose state-space data assume a very general nonlinear dependence in the uncertain parameters. Our resolution methods rely on new linear matrix inequality characterizations of /spl Hscr//sub 2/ and /spl Hscr//sub /spl infin// performances, which, in conjunction with suitable linearization transformations of the variables, give rise to practical and computationally tractable formulations for the robust filtering problem.
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