Dynamical Robust Nonlinear H{sub}∞ Filtering for Lipschitz Descriptor Systems with Parametric and Nonlinear Uncertainties

摘要

In this paper, a dynamical robust nonlinear H{sub}∞ filtering method is proposed for a class of Lipschitz descriptor systems in which the nonlinearities appear in both the state and measured output equations. The system is assumed to have norm-bounded uncertainties in the realization matrices as well as nonlinear uncertainties. We synthesis the H{sub}∞ observer through semidefinite programming and strict LMIs. The admissible Lipschitz constants of the nonlinear functions are maximized through LMI optimization. The resulting H{sub}∞ observer 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 norm-wise robustness analysis.