# Nonlinear moving horizon estimation for robust state and parameter estimation -- extended version

arXiv: https://arxiv.org/abs/2312.13175 · HTML: https://mlsanity.com/paper/2312.13175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operator](https://mlsanity.com/paper/2604.01802/) (2026)

## Abstract (arXiv)

We propose a moving horizon estimation scheme to estimate the states and the unknown constant parameters of general nonlinear uncertain discrete-time systems. The proposed framework and analysis explicitly do not involve the a priori verification of a particular excitation condition for the parameters. Instead, we use online information about the actual excitation of the parameters at any time during operation and ensure that the regularization term in the cost function is always automatically selected appropriately. This ensures that the state and parameter estimation error is bounded for all times, even if the parameters are never (or only rarely) excited during operation. Robust exponential stability of the state and parameter estimation error emerges under an additional uniform condition on the maximum duration of insufficient excitation. The theoretical results are illustrated by a numerical example.