5 papers
Efficient stochastic model-predictive control based on the meta-state-space representation
Bendegúz Györök, Roland Tóth, Maarten Schoukens +1
Stochastic model-predictive control (SMPC) has evolved to a powerful framework for the control of stochastic dynamical systems. SMPC utilizes a probabilistic uncertainty descriptio…
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
Yuhan Liu, Máté Kiss, Roland Tóth +1
While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems where robustness to extrapolation/interpolation errors…
Efficient identification of linear, parameter-varying, and nonlinear systems with noise models
Alberto Bemporad, Roland Tóth
We present a general system identification procedure capable of estimating of a broad spectrum of state-space dynamical models, including linear time-invariant (LTI), linear parame…
A Direct State-Space Realization of Discrete-Time Linear Parameter-Varying Input-Output Models
Johan Kon, Roland Tóth, Jeroen van de Wijdeven +2
A minimal state-space (SS) realization of an identified linear parameter-varying (LPV) input-output (IO) model usually introduces dynamic and nonlinear dependency of the state-spac…
Port-Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Gerben I. Beintema, Nick Jaensson +2
Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural net…