6 papers
Robust Space-Filling Input Design via Stochastic Optimization
Máté Kiss, Roland Tóth, Maarten Schoukens
The space-filling input design approach generates a so-called space-filling dataset in the feature space of the system model. The design method is applicable on a broad class of mo…
Least Costly Space-Filling Experiment Design for the Identification of a Nonlinear System
Máté Kiss, Maarten Schoukens, Roland Tóth
The quality of an estimated nonlinear model highly depends on the data quality that was used for the system identification. By using a Gaussian Process-based optimal input design a…
Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms
Péter Antal, Andrea Carron, Melanie Zeilinger +2
This paper presents a novel model predictive control (MPC) approach for autonomous pick-and-place between moving platforms with a hook-equipped aerial manipulator. First, for accur…
Data-driven Learning of LPV Surrogate Models of Fuel Sloshing
E. Javier Olucha, Valentin Preda, Amritam Das +1
This paper aims to enhance the efficiency of validation and verification campaigns involving fuel sloshing phenomena. Our first contribution is the development of an open-source, h…
Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
Bendegúz Györök, Roel Drenth, Chris Verhoek +3
The integration of first-principles models with learning-based components, i.e., model augmentation, has gained increasing attention, as it offers higher model accuracy and faster…
Learning Surrogate LPV State-Space Models with Uncertainty Quantification
E. Javier Olucha, Valentin Preda, Amritam Das +1
The Linear Parameter-Varying (LPV) framework enables the construction of surrogate models of complex nonlinear and high-dimensional systems, facilitating efficient stability and pe…