Event-Triggered State Estimation of Linear Systems Using Moving Horizon Estimation

In this brief, a problem of event-triggered state estimation for networked linear systems is investigated. We consider that the stochastic system disturbances and noise are bounded and moving horizon estimation (MHE) is used to handle these constraints. We establish an event-based state estimation mechanism that aims to provide good state estimates while reducing the frequencies of both the evaluation of the state estimator and networked communication between the plant and the estimator. An event-triggering condition is used to govern the evaluation of the MHE-based estimator and the use of networked communication. An MHE-based estimator is designed to provide state estimates when there is an event. Stability analysis of the estimation error dynamics is carried out for the proposed event-triggered estimation mechanism. The effectiveness and the applicability of the proposed method are demonstrated through numerical simulations and an experiment.

Automated summary

This paper investigates the problem of event-triggered state estimation for networked linear systems, proposing an event-based MHE estimator to provide good state estimates by reducing the frequency of state estimator evaluations and network communications.