9 papers · 1 filter
Inference in Latent Force Models Using Optimal State Estimation
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller +1
Latent force models, a class of hybrid modeling approaches, integrate physical knowledge of system dynamics with a latent force - an unknown, unmeasurable input modeled as a Gaussi…
Data-based Moving Horizon Estimation under Irregularly Measured Data
Tobias M. Wolff, Isabelle Krauss, Victor G. Lopez +1
In this work, we introduce a sample- and data-based moving horizon estimation framework for linear systems. We perform state estimation in a sample-based fashion in the sense that…
Estimating Hormone Concentrations in the Pituitary-Thyroid Feedback Loop from Irregularly Sampled Measurements
Seth Siriya, Tobias M. Wolff, Isabelle Krauss +2
Model-based control techniques have recently been investigated for the recommendation of medication dosages to address thyroid diseases. These techniques often rely on knowledge of…
Data-driven stabilization of nonlinear systems via descriptor embedding
Mohammad Alsalti, Claudio De Persis, Victor G. Lopez +1
We introduce the notion of descriptor embedding for nonlinear systems and use it for the data-driven design of stabilizing controllers. Specifically, we provide sufficient data-dep…
Robust stability of event-triggered nonlinear moving horizon estimation
Isabelle Krauss, Victor G. Lopez, Matthias A. Müller
In this work, we propose an event-triggered moving horizon estimation (ET-MHE) scheme for the remote state estimation of general nonlinear systems. In the presented method, wheneve…
Local Observability of a Class of Feedforward Neural Networks
Yi Yang, Victor G. Lopez, Matthias A. Müller
Beyond the traditional neural network training methods based on gradient descent and its variants, state estimation techniques have been proposed to determine a set of ideal weight…
On sample-based functional observability of linear systems
Isabelle Krauss, Victor G. Lopez, Matthias A. Müller
Sample-based observability characterizes the ability to reconstruct the internal state of a dynamical system by using limited output information, i.e., when measurements are only i…
Sufficient Conditions for Detectability of Approximately Discretized Nonlinear Systems
Seth Siriya, Julian D. Schiller, Victor G. Lopez +1
In many sampled-data applications, observers are designed based on approximately discretized models of continuous-time systems, where usually only the discretized system is analyze…
Insights into the explainability of Lasso-based DeePC for nonlinear systems
Gianluca Giacomelli, Simone Formentin, Victor G. Lopez +2
Data-enabled Predictive Control (DeePC) has recently gained the spotlight as an easy-to-use control technique that allows for constraint handling while relying on raw data only. In…