50 citations · 106 across the 11 of their papers we have counts for
12 papers
Feedback Synthesis for Nonlinear Systems Via Convex Control Lyapunov Functions
Mario Eduardo Villanueva, Juraj Oravec, Radoslav Paulen +1
This paper introduces computationally efficient methods for synthesizing explicit piecewise affine (PWA) feedback laws for nonlinear discrete-time systems, ensuring robustness and…
Carbon Neutral Greenhouse: Economic Model Predictive Control Framework for Education
Marek Wadinger, Rastislav Fáber, Erika Pavlovičová +1
This paper presents a comprehensive framework aimed at enhancing education in modeling, optimal control, and nonlinear Model Predictive Control~(MPC) through a practical greenhouse…
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.,…
Tube-enhanced Multi-stage MPC for Flexible Robust Control of Constrained Linear Systems with Additive and Parametric Uncertainties
Sankaranarayanan Subramanian, Sergio Lucia, Radoslav Paulen +1
The trade-off between optimality and complexity has been one of the most important challenges in the field of robust Model Predictive Control (MPC). To address the challenge, we pr…
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…
Bayesian Approach to Probabilistic Design Space Characterization: A Nested Sampling Strategy
Kennedy P. Kusumo, Lucian Gomoescu, Radoslav Paulen +4
Quality by design in pharmaceutical manufacturing hinges on computational methods and tools that are capable of accurate quantitative prediction of the design space. This paper inv…
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…
Optimal Experiment Design in Nonlinear Parameter Estimation with Exact Confidence Regions
Anwesh Reddy Gottu Mukkula, Radoslav Paulen
A model-based optimal experiment design (OED) of nonlinear systems is studied. OED represents a methodology for optimizing the geometry of the parametric joint-confidence regions (…
Interval Superposition Arithmetic for Guaranteed Parameter Estimation
Junyan Su, Yanlin Zha, Kai Wang +3
The problem of guaranteed parameter estimation (GPE) consists in enclosing the set of all possible parameter values, such that the model predictions match the corresponding measure…