Generalized Nonlinear Robust Energy-to-Peak Filtering for Differential Algebraic Systems

TL;DR

This paper develops a generalized nonlinear energy-to-peak filter for uncertain descriptor systems, using semidefinite programming and LMIs to optimize filtering performance with more flexible structures.

Contribution

It introduces a novel nonlinear dynamic filtering approach with increased degrees of freedom for uncertain descriptor systems, improving robustness and performance.

Findings

Filter synthesis via semidefinite programming and LMIs

Enhanced robustness for systems with nonlinearities and uncertainties

Optimized energy-to-peak filtering performance

Abstract

The problem of robust nonlinear energy-to-peak filtering for nonlinear descriptor systems with model uncertainties is addressed. The system is assumed to have nonlinearities both in the state and output equations as well as norm-bounded time-varying uncertainties in the realization matrices. A generalized nonlinear dynamic filtering structure is proposed for such a class of systems with more degrees of freedom than the conventional static-gain and dynamic filtering structures. The L2-Linfty filter is synthesized through semidefinite programming and strict LMIs, in which the energy-to-peak filtering performance in optimized.