G
Gašper Mušič
Researcher at University of Ljubljana
Publications - 73
Citations - 654
Gašper Mušič is an academic researcher from University of Ljubljana. The author has contributed to research in topics: Petri net & Model predictive control. The author has an hindex of 14, co-authored 68 publications receiving 591 citations.
Papers
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Journal ArticleDOI
Hybrid fuzzy model-based predictive control of temperature in a batch reactor
TL;DR: A comparison between MPC employing a hybrid linear model and a hybrid fuzzy model was made and it was established that the latter approach clearly outperforms the approach where a linear model is used.
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Original Articles: Coloured Petri net scheduling models: Timed state space exploration shortages
M. A. Piera,Gašper Mušič +1 more
TL;DR: It is shown that the established simulation techniques do not perform adequately in some application relevant examples since in general, only a subset of a timed state space of a simulated system is represented.
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Experimental design of an optimal phase duration control strategy used in batch biological wastewater treatment.
N. Pavšelj,Nadja Hvala,Juš Kocijan,Juš Kocijan,M. Roš,M. Šubelj,Gašper Mušič,Stanko Strmčnik +7 more
TL;DR: The design of an algorithm used in control of a sequencing batch reactor (SBR) for wastewater treatment for on-line optimization of the batch phases duration which should be applied due to the variable input wastewater improves the treatment quality and reduces energy consumption.
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Hybrid modelling and optimal control of a Multiproduct Batch Plant
TL;DR: This paper addresses the problem of optimally selecting the production plan for a Multiproduct Batch Plant using the high level modelling language, HYbrid System DEscription Language (HYSDEL), and takes into account a model of a hybrid system described as an MLD system.
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Production-process modelling based on production-management data: a Petri-net approach
Dejan Gradišar,Gašper Mušič +1 more
TL;DR: This paper describes how to apply timed Petri nets and existing production data to the modelling of production systems and describes a method for using these data to construct a Petri-net model algorithmically.