We use physics-informed neural networks (PINNs) to numerically solve the discrete-time
nonlinear observer-based state estimation problem. Integrated within a single-step exact …

This paper presents a first step towards tuning observers for general nonlinear systems.
Relying on recent results around Kazantzis-Kravaris/Luenberger (KKL) observers, we …

This paper proposes a Kazantzis–Kravaris–Luenberger (KKL) observer design for discrete-
time nonlinear systems whose output is affected by a time-varying measurement delay …

For controlling nonlinear processes represented by state‐space models, a state observer is
needed to estimate the states from the trajectories of measured variables. While model …

This paper focuses on the model-free synthesis of state observers for nonlinear autonomous
systems without knowing the governing equations. Specifically, the Kazantzis …

Reinforcement learning (RL) excels in applications such as video games, but ensuring
safety as well as the ability to achieve the specified goals remains challenging when using …

This paper proposes a novel learning approach for designing Kazantzis-
Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a …

Learning complex trajectories from demonstrations in robotic tasks has been effectively
addressed through the utilization of Dynamical Systems (DS). State-of-the-art DS learning …

Neural Ordinary Differential Equations (ODEs) offer potential for learning continuous
dynamics, but their slow training and inference limit broader use. This paper proposes …

State observation is necessary for feedback control but often challenging for nonlinear
systems. While Kazantzis-Kravaris/Luenberger (KKL) observer gives a generic design, its …