Event-based state and fault estimation for stochastic nonlinear system with Markov packet dropout

This paper investigates the event-based state and fault estimation problem for stochastic nonlinear system with Markov packet dropout. By introducing the fictitious noise, the fault is augmented to the system state. Then combining the unscented Kalman filter (UKF) with event-triggered and Markov packet dropout, the modified UKF is proposed to estimate the state and fault. Meanwhile, the stochastic stability of the proposed filter is also discussed. Finally, two simulation results illustrate the performance of the proposed method. (C) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the problem of event-based state and fault estimation for stochastic nonlinear systems with Markov packet dropout. By introducing fictitious noise, the fault is integrated into the system state, and a modified unscented Kalman filter (UKF) is proposed to estimate the state and fault. The stochastic stability of the proposed filter is also discussed, and two simulation results are presented to illustrate the performance of the proposed method.