13 citations · 21 across the 5 of their papers we have counts for
6 papers
Data-Based Design of Multi-Model Inferential Sensors
Martin Mojto, Karol Lubušký, Miroslav Fikar +1
This paper deals with the problem of inferential (soft) sensor design. The nonlinear character of industrial processes is usually the main limitation to designing simple linear inf…
Design of Multi-model Linear Inferential Sensors with SVM-based Switching Logic
Martin Mojto, Miroslav Fikar, Radoslav Paulen
We study the problem of data-based design of multi-model linear inferential (soft) sensors. The multi-model linear inferential sensors promise increased prediction accuracy yet sim…
Data-based Design of Inferential Sensors for Petrochemical Industry
Martin Mojto, Karol Ľubušký, Miroslav Fikar +1
Inferential (or soft) sensors are used in industry to infer the values of imprecisely and rarely measured (or completely unmeasured) variables from variables measured online (e.g.,…
Robust multi-stage model-based design of optimal experiments for nonlinear estimation
Anwesh Reddy Gottu Mukkula, Michal Mateáš, Miroslav Fikar +1
We study approaches to robust model-based design of experiments in the context of maximum-likelihood estimation. These approaches provide robustification of model-based methodologi…
Dynamic Real-time Optimization of Batch Processes using Pontryagin's Minimum Principle and Set-membership Adaptation
Radoslav Paulen, Miroslav Fikar
This paper studies a dynamic real-time optimization in the context of model-based time-optimal operation of batch processes under parametric model mismatch. In order to tackle the…
Dual-control based approach to batch process operation under uncertainty based on optimality-conditions parameterization
Radoslav Paulen, Miroslav Fikar
This paper presents a scheme for dual robust control of batch processes under parametric uncertainty. The dual-control paradigm arises in the context of adaptive control. A trade-o…