Decentralized event-triggered estimation of nonlinear systems

TL;DR

This paper develops a decentralized event-triggered estimation approach for nonlinear systems, enabling efficient remote state estimation with sporadic communication, ensuring stability and avoiding Zeno behavior.

Contribution

It introduces a systematic design of local dynamic event-triggering rules for nonlinear systems that do not require observer duplication, ensuring practical convergence and minimum inter-event times.

Findings

Proves practical convergence of estimation error to the origin.

Establishes existence of a positive minimum inter-event time.

Demonstrates effectiveness on a robotic arm case study.

Abstract

We investigate the scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. The sensors are grouped into N nodes and each of these nodes decides when its measured data is transmitted over the network independently. The objective is to design both the observer and the local transmission policies in order to obtain accurate state estimates, while only sporadically using the communication network. In particular, given a general nonlinear observer designed in continuous-time satisfying an input-to-state stability property, we explain how to systematically design a dynamic event-triggering rule for each sensor node that avoids the use of a copy of the observer, thereby keeping local calculation simple. We prove the practical convergence property of the estimation error to the origin and we show that there exists a uniform strictly positive minimum inter-event time for each local triggering rule under mild conditions on the plant. The efficiency of the proposed techniques is illustrated on a numerical case study of a flexible robotic arm.