  A generalized dynamical robust nonlinear filtering framework is established
for a class of Lipschitz differential algebraic systems, in which the
nonlinearities appear both in the state and measured output equations. The
system is assumed to be affected by norm-bounded disturbance and to have both
norm-bounded uncertainties in the realization matrices as well as nonlinear
model uncertainties. We synthesize a robust H_infty filter through semidefinite
programming and strict linear matrix inequalities (LMIs). The admissible
Lipschitz constants of the nonlinear functions are maximized through LMI
optimization. The resulting H_infty filter guarantees asymptotic stability of
the estimation error dynamics with prespecified disturbance attenuation level
and is robust against time-varying parametric uncertainties as well as
Lipschitz nonlinear additive uncertainty. Explicit bound on the tolerable
nonlinear uncertainty is derived based on a norm-wise robustness analysis.
