2 citations · 2 across the 7 of their papers we have counts for
8 papers
Safety and Security: Experimental Validation of Encrypted Model Predictive Control
Juraj Holaza, Martin Kalúz, Matúš Furka +2
In this paper, we revisit the problem of an encrypted model predictive control (MPC) design, representing a significant challenge in the recent field of secure process control. Exi…
Star-Tracker-Constrained Attitude MPC for CubeSats
Dominik Beňo, Patrik Valábek, Martin Hromčík +1
This paper presents an online linear model predictive control (MPC) framework for slew maneuvers that maintains star-tracker availability during ground-target tracking. The nonline…
DeePC vs. Koopman MPC for Pasteurization: A Comparative Study
Branislav Daráš, Patrik Valábek, Martin Klaučo
Data-driven predictive control methods can provide the constraint handling and optimization of model predictive control (MPC) without first-principles models. Two such methods diff…
Deep Dictionary-Free Method for Identifying Linear Model of Nonlinear System with Input Delay
Patrik Valábek, Marek Wadinger, Michal Kvasnica +1
Nonlinear dynamical systems with input delays pose significant challenges for prediction, estimation, and control due to their inherent complexity and the impact of delays on syste…
Deep Koopman Economic Model Predictive Control of a Pasteurisation Unit
Patrik Valábek, Michaela Horváthová, Martin Klaučo
This paper presents a deep Koopman-based Economic Model Predictive Control (EMPC) for efficient operation of a laboratory-scale pasteurization unit (PU). The method uses Koopman op…
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems
Jan Drgona, Karol Kis, Aaron Tuor +2
We present differentiable predictive control (DPC) as a deep learning-based alternative to the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC fra…
Neural Network Based Explicit MPC for Chemical Reactor Control
Karol Kiš, Martin Klaučo
In this paper, we show the implementation of deep neural networks applied in process control. In our approach, we based the training of the neural network on model predictive contr…
Non-linear Model Predictive Control of Conically Shaped Liquid Storage Tanks
Martin Klaučo, Ľuboš Čirka
This paper deals with the analysis and synthesis of a model predictive control (MPC) strategy used in connection with level control in conically shaped industrial liquid storage ta…