Event-triggered moving horizon estimation for nonlinear systems

TL;DR

This paper introduces an event-triggered moving horizon estimation scheme for nonlinear systems that reduces computational load by updating estimates only when necessary, maintaining stability and accuracy.

Contribution

It presents a novel event-triggering mechanism and cost function design for nonlinear MHE, enabling less frequent computations without sacrificing estimation performance.

Findings

Achieved 86% reduction in computational resources compared to standard MHE.

Proved robust global exponential stability under detectability conditions.

Demonstrated effectiveness on a nonlinear benchmark example.

Abstract

This work proposes an event-triggered moving horizon estimation (ET-MHE) scheme for general nonlinear systems. The key components of the proposed scheme are a novel event-triggering mechanism (ETM) and the suitable design of the MHE cost function. The main characteristic of our method is that the MHE's nonlinear optimization problem is only solved when the ETM triggers the transmission of measured data to the remote state estimator. If no event occurs, then the current state estimate results from an open-loop prediction using the system dynamics. Furthermore, we show robust global exponential stability of the ET-MHE under a suitable detectability condition. Finally, we illustrate the applicability of the proposed method in terms of a nonlinear benchmark example, where we achieved similar estimation performance compared to standard MHE using 86% less computational resources.