Robust Kalman Predictors With Asymptotic Equivalence and Absolute Asymptotic Stability for Networked Uncertain System With Hard and Soft Sensors

For networked uncertain systems (NUSs), an improved unified sensor model with “hard” and “soft” sensors including five uncertainties is presented, where two-step random measurement delays and colored measurement noise are considered. The minimax robust time-varying and steady-state Kalman predictors are presented in the sense that their actual prediction error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. New concepts of their asymptotic equivalence and absolute asymptotic stability are presented and proved by the dynamic error system analysis (DESA) method and the dynamic variance error system analysis (DVESA) method. They constitute a novel robust Kalman filtering and asymptotic stability theory for NUSs, and overcome the limitation of the classical Kalman filtering. One simulation example applied to the vehicle suspension system verifies the effectiveness of the proposed theory.