Robust Stability of Suboptimal Moving Horizon Estimation using an Observer-Based Candidate Solution

TL;DR

This paper introduces a suboptimal moving horizon estimator for nonlinear systems that guarantees robust stability using an observer-based candidate solution, independent of horizon length or optimization execution.

Contribution

It transfers the feasibility-implies-stability paradigm from model predictive control to moving horizon estimation, ensuring stability without full optimization.

Findings

Robust stability is achieved without optimization execution.

Stability is independent of horizon length.

The approach is applicable to nonlinear systems.

Abstract

In this paper, we propose a suboptimal moving horizon estimator for nonlinear systems. For the stability analysis we transfer the "feasibility-implies-stability/robustness" paradigm from model predictive control to the context of moving horizon estimation in the following sense: Using a suitably defined, feasible candidate solution based on the trajectory of an auxiliary observer, robust stability of the proposed suboptimal estimator is inherited independently of the horizon length and even if no optimization is performed.