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Projects / Programmes source: ARIS

Systems and control

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Periods
January 1, 2022 - December 31, 2027
Research activity

Code Science Field Subfield
2.06.00  Engineering sciences and technologies  Systems and cybernetics   

Code Science Field
2.02  Engineering and Technology  Electrical engineering, Electronic engineering, Information engineering 
Keywords
Control systems engineering, advanced process control, nonlinear systems identification, condition monitoring, prognostics and health management, machine learning, smart factories, hydrogen technologies, tokamak reactor, wastewater treatment, air pollution monitoring
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Points
6,514.92
A''
1,369.94
A'
3,170.44
A1/2
4,444.42
CI10
4,286
CImax
173
h10
32
A1
23.36
A3
14.13
Data for the last 5 years (citations for the last 10 years) on April 27, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender , archive )
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Database Linked records Citations Pure citations Average pure citations
WoS  347  4,447  3,832  11.04 
Scopus  502  6,501  5,636  11.23 
Researchers (31)
no. Code Name and surname Research area Role Period No. of publications
1.  34624  PhD Pavle Boškoski  Systems and cybernetics  Researcher  2022 - 2024  176 
2.  54686  Martin Brešar  Systems and cybernetics  Junior researcher  2022 - 2024  5 
3.  28726  Stanislav Černe    Technical associate  2022 - 2024  41 
4.  15735  PhD Gregor Dolanc  Systems and cybernetics  Researcher  2022 - 2024  219 
5.  29965  Primož Fajdiga    Technical associate  2022 - 2024  30 
6.  16161  PhD Samo Gerkšič  Systems and cybernetics  Researcher  2022 - 2024  136 
7.  33316  PhD Miha Glavan  Systems and cybernetics  Researcher  2022 - 2024  95 
8.  04944  PhD Giovanni Godena  Systems and cybernetics  Researcher  2022 - 2024  234 
9.  58833  Žan Gorenc  Systems and cybernetics  Junior researcher  2023 - 2024  0 
10.  22483  PhD Dejan Gradišar  Systems and cybernetics  Researcher  2022 - 2024  161 
11.  55764  Žiga Gradišar  Systems and cybernetics  Junior researcher  2022 - 2024  0 
12.  05807  PhD Nadja Hvala  Systems and cybernetics  Researcher  2022 - 2024  208 
13.  28890  Maja Janežič    Technical associate  2022 - 2024  0 
14.  35947  David Jure Jovan    Technical associate  2022 - 2024  20 
15.  08351  PhD Vladimir Jovan  Systems and cybernetics  Researcher  2022  381 
16.  02561  PhD Đani Juričić  Systems and cybernetics  Head  2022 - 2024  414 
17.  10598  PhD Juš Kocijan  Systems and cybernetics  Researcher  2022 - 2024  450 
18.  52049  PhD Tadej Krivec  Systems and cybernetics  Researcher  2022 - 2023  17 
19.  54699  Jernej Mlinarič  Systems and cybernetics  Junior researcher  2022 - 2024  7 
20.  28466  PhD Marko Nerat  Systems and cybernetics  Researcher  2022  41 
21.  39149  PhD Gjorgji Nusev  Systems and cybernetics  Technical associate  2022  32 
22.  57080  Aljaž Pavšek  Systems and cybernetics  Junior researcher  2022 - 2024  22 
23.  29924  PhD Matija Perne  Systems and cybernetics  Researcher  2022 - 2024  131 
24.  04543  PhD Janko Petrovčič  Systems and cybernetics  Researcher  2022 - 2024  325 
25.  25655  PhD Boštjan Pregelj  Systems and cybernetics  Researcher  2022 - 2024  128 
26.  57092  Matic Rutnik  Systems and cybernetics  Junior researcher  2022 - 2024  0 
27.  51226  Žiga Stržinar  Systems and cybernetics  Junior researcher  2022 - 2024  20 
28.  15583  Miroslav Štrubelj    Technical associate  2022 - 2024  30 
29.  12342  PhD Damir Vrančić  Systems and cybernetics  Researcher  2022 - 2024  339 
30.  19031  PhD Darko Vrečko  Systems and cybernetics  Researcher  2022 - 2024  158 
31.  52069  Luka Žnidarič  Systems and cybernetics  Researcher  2022 - 2024  4 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  90,753 
Abstract
The underlying programme is aimed to create knowledge to respond to the emerging societal needs on a national and European scale. It will be based on inter-disciplinary endeavour and cross-fertilization of challenges in research and innovation with the critical societal challenges. The programme's ambitions are compliant with major European strategies like Horizon Europe, SPIRE, EFFRA and national strategies (Slovenian Smart Specialisation Strategy). The programme will focus on the development of methodology and tools for the design and implementation of advanced control systems with the ultimate goal to maximize the performance, quality, durability, reliability and safety of the processes in the sectors of priority concern. The programme consists of four horizontal tracks dealing with methods and building blocks for control and three vertical tracks related to applied research in priority areas. The former include: (i) modelling complex dynamic systems, (ii) advanced control, (iii) prognostics and health management and (iv) special modules for control systems implementation, while the latter refer to the domains of (i) clean energies, (ii) smart factories and (iii) clean environment. Main research challenges concern inverse problems where immeasurable quantities have to be inferred from large streams of measured data. Problem solving will rely on probabilistic learning techniques will be used. To optimize system performance by accounting for constraints, predictive control solutions will be used. Applications on the priority domain will stand on solid domain knowledge and close cooperation with domain experts from our rich network of academic and industrial partners. A substantial societal and economic impact is expected from our Programme. The group will continue to play a leading role in mobilizing national R&D resources through consortia and networks with more than 40 national industrial partners and including 2 centres of excellence, competence centre, technological centre ARI and Strategic Research and Innovation Partnership of the Factories of the Future. Our solutions are expected to produce a multifaceted impact on business and profit not only leading Slovenian companies (Kolektor, Petrol, Domel, Danfoss Trata, HESS, Acroni, etc.) but even more many smaller niche manufacturers and systems integrators (e.g. INEA, Sisteh) that vitally depend on the high value-added solutions. We will continue with the growing spread of our results to EU companies together with our EU partners through the EU programme, EDA and SPIRE. We will pursue contributing control solutions for the emerging generations of the technologies, like fuel cells and electrolysers through the collaboration with top EU players (CEA, VTT, EPFL, SolidPower etc.). A new niche, emerging thereof, refers to the design and implementation of methodologies and new items and equipment to accelerate digitalisation of the laboratories in the domain of fuel cells at our partners.
Significance for science
The field of control system technologies, as part of the Information and Communication Technologies, plays a pivotal role in shaping practically all sectors of the economy and society. As such it has become a domain of highest priority on the global scale and in the EU and Slovenia in particular. Our group has been contributing knowledge to respond to the emerging societal needs on a national and European level. Successful collaboration with distinctive academic and industrial partners in the EU has been established in the previous years and this is expected to keep growing. One of the important focuses of our programme group will remain to solve inverse problems where inference about unknown quantities is done from measured data. In doing so a consistent Bayesian framework will be used. Our group is renowned for the Gaussian process (GP) model approach, a probabilistic machine learning technique for non-linear dynamic systems. Further advancement of computationally efficient GP modelling for the class of spatio-temporal landscapes is expected. These methods will substantially expand the toolset for modelling problems in the domains like radiological pollution monitoring after a nuclear accident, which are of high interest for the safety of the population during nuclear power production. Besides GP models, we will address learning a rather peculiar class of fractional-order systems (FOS) models. FOS appear in several important domains like fuel cells, batteries, social systems and material science. Instead of conventional differential operators, they employ fractional differentiation. Despite increased interest in recent years, very little has been done in their learning from data. To fill the gap, we will address the unsolved problems, i.e. the problem of structural and parametric identification of FOS models from data in the Bayesian framework. Our interest will remain in model predictive control to optimise the system operation up to the edge of the performance limits. Examples span systems with complex dynamics like advanced electro-mechanical systems, fusion and nuclear reactors, real-time optimizing control of fuel cell and electrolyzer systems as well as interconnected distributed systems like smart grids. Strong motivation to pursue the research arises from our recent accomplishments related to the MPC design for the ITER tokamak reactor, which proved superior to conventional approaches. Research on MPC for the class of large-scale multivariable systems with dynamics in the sub-millisecond range will be pursued. The idea is to exploit the rising computational power of state-of-the-art processor technologies with innovative MPC algorithms design, which will ensure the computational time shorter than the sampling interval. Applications in fast laboratory tokamaks are envisaged. In the area of prognostics and health management, we have contributed notable results validated on electrochemical conversion systems and rotational machines. We will summarise the experience and develop an evolving PHM framework applicable to a particular class of items of equipment, e.g. fuel cells. The key idea is to fuse information from past run-to-failure operational data and hence gradually improve the diagnostic and prognostic models- Such models are then applied to the newly implemented devices. Hybrid data mining will be applied to take into account all the available discrete and continuous sensor readings as well as categorical data from maintenance actions. No systematic solution to this problem exists at the time being. Our recent research in diagnosis and prognosis of fuel cells, electrolysers and batteries has resulted in an efficient technique for their characterisation referred to as fast electrochemical impedance spectroscopy. Also, we were the first to present an algorithm for online prediction of the remaining useful life of solid oxide fuel cells. Our methods show great potential to improve the reliability and durability of electrochemical conversion systems. PHM research in most of the emerging fuel cells and electrolysers technologies are still in the embryonic stage and many hard problems have to be solved. Contribution is expected through our evolving PHM framework will be accommodated for several classes of hydrocarbon-fuelled PEM fuel cells, solid oxide fuel cells and solid oxide electrolysis cells. The expected results are important to manufacturers for better quality control as well as optimizing the maintenance strategies. Our research in complex systems modelling and system diagnosis shows potential for extension to the domain of human health monitoring. The initial collaboration with the University Medical Centre Ljubljana indicates the signal processing and change detection enable quantitative assessment of the health status of persons with cardiovascular impair. That is expected to result in the design of more efficient therapy on the individual level. The methods for data-driven complex systems modelling bring new research potentials in the domain of social development. An example is a difficult problem of predicting the labour market and evaluation of the employment probability of job seekers by using historical data records. Initial efforts indicate promising results could be expected in this indeed challenging area.
Significance for the country
In the context of sustainable measures of the EU and Slovenia to improve the long-term sustainability of development and quality of life, the results of our programme will have the following techno-economical and social effects on: o accelerating productivity growth for economic progress and raising the living standards of the population. Here, the work on the programme will contribute to the acceleration of digitalisation of the economy, especially by introducing the concept of Smart Factories in our industrial partners. Industrial diagnostics and condition monitoring methods are one of the key components of the Factories of the Future and have exceptional market potential, provided that their installation and use are relatively easy. Therefore, we will focus our development on the greatest possible universality of methods, the ability to adapt to various industrial processes and relatively easy installation. We will achieve this through the concepts of minimal-invasiveness and self-development. With the developed methods, it will be possible to equip many production processes, using the market channels of existing global manufacturers of automation equipment (e.g. Festo, Mitsubishi), we have already cooperated with some of them. Also, the demand from final users is significant, we have been contacted by many companies (Helios, Donit, Polycom, Talum, …). Our systems for diagnostics and monitoring of rotary machines have been used for many years in end-of-line quality inspection systems, which we have developed together with end-user companies (Domel, Podkrižnik). We plan further development and breakthrough in the direction of reconfigurability and flexibility, which will drastically increase the possibility of more mass marketing, as the systems will be relatively easy to adapt and thus reach different end-users. As Slovenia is an export-oriented country, where exports represent around 40% of gross domestic product, our participatinon in the realization of new demanding products or services will raise the competitiveness of our export companies on the global market. It should also be mentioned that the employees of our programme group, who continue their professional careers in the economy, are highly sought after and valued, and are employed in responsible positions; o transition to a low-carbon circular economy. In this area, we will contribute to decarbonisation and a cleaner environment by working on the introduction of green technologies in industry and society (production of green hydrogen, balancing of the electro-energetic network, optimization of the operation of waste water treatment plants). Due to their properties (short measurement time, affordable measuring equipment, minimally invasive principle), our algorithms and measuring equipment for prognostics and condition monitoring (PHM) of fuel cells and other electrochemical systems have exceptional potential for use in both research laboratories and in commercial mass-produced electrochemical systems. The systems are already used in the laboratories of our partners (CEA, TU Graz), and we plan to penetrate other laboratories of research institutions (e,g, EPFL) and fuel cell manufacturers (e.g. SolidPower). With the penetration of fuel cells and batteries into mass use, a need for diagnostics will appear and increase, which will represent a tremendous potential for the use of our PHM technology in embedded systems. Next, our MPC control algorithms for Tokamak reactors (ITER) have great potential for use in research reactors even before the first fusion power plants are built. With algorithms and appropriate hardware, it will be possible to equip a number of experimental reactors being built around the world and to test different fusion control strategies. Market entry would take place through companies involved in the instrumentation of physical experimental systems (one of which is Cosylab that we collaborate with). Our technology for plant-wide supervisory control will contribute to further digitalisation of Slovenian wastewater treatment plants WWTPs, in particular in what concerns solving the top-level WRRF optimisation, where newly emerging objectives of resource recovery, climate issues and the serious problem of contaminants of emerging concern (for which there is no solution at the moment). will be considered. Additional economic benefits concern reduced use of electrical energy by 5% and reduced use of chemicals and increased overall efficiency of cleaning especially in extreme conditions. Our activities on modelling radiological pollution will have an impact for Slovenia, for IAEA (International Atomic Energy Agency) as well as and for interested nuclear power plants worldwide. On the user side, the developed model will summarize all the key information about the long-term dynamics of received radiation doses in case of a nuclear accident, as required by Updated Safety Analysis Report (USAR) in a periodic review for nuclear power plants. In addition, this research may provide the information basis for the discussion on the possibilities and potential viability of building a new nuclear power plant in Krško, Slovenia; o strengthening the development role of the state and its institutions. Through active work in several interest-based associations in the field of Factories of the Future (SRIP Factories of the Future, Competence Centre Systems and Control Technologies) and low-carbon technologies (Centre of Excellence Low-Carbon Technologies, Development Centre for Hydrogen Technologies) we will influence on the development policies of the state by cooperating with state institutions. We will participate in the preparation of development strategies and priorities in the above-mentioned areas; o adapting to demographic change to ensure a dignified life. By introducing the concept of Factories of the Future and our achieved results in this research field, we will contribute to the humanization of the work environment.
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