Identifying a nonlinear dynamic systems' governing equation is crucial for many engineering
applications, and yet a challenging task. In this study, the system's dynamics are …

Contraction theory formulates the analysis of nonlinear systems in terms of Jacobian
matrices. Although this provides the potential to develop a linear matrix inequality (LMI) …

Nonlinear adaptive observers that do not require knowledge of the dynamics of the system
being observed are proposed. This objective is pursued by envisioning a linear-in …

We study a class of recursive least-squares estimators in an errors-in-variables setting
where disturbances affect both the regressor and the regressand variables. We prove the …

High gain observers are often used for the real-time estimation of the state of nonlinear
systems. Several design methods are based on normal forms, which are based on …

Model‐based state observers require high‐quality models to deliver accurate state
estimates. However, due to time or cost shortage, modeling simplifications or numerical …

Due to its state-of-the-art estimation performance complemented by rigorous and non-
conservative uncertainty bounds, Gaussian process regression is a popular tool for …

In this paper, we introduce a Gaussian process based moving horizon estimation (MHE)
framework. The scheme is based on offline collected data and offline hyperparameter …

State estimation when only a partial model of a considered system is available remains a
major challenge in many engineering fields. This work proposes a joint, square-root …

In this paper, we propose a novel Gaussian process-based moving horizon estimation
(MHE) framework for unknown nonlinear systems. In the proposed scheme, we take …