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H2020Докторантска мрежа2021–2025

ELO-X · Embedded learning and optimization for the next generation of smart industrial control systems

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2021-01-01 → 2025-06-30
Финансиране от ЕС
3 884 354 €
Участници
11
Схема
MSCA-ITN

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Накратко на български

Интелигентните индустриални системи за управление се развиват чрез алгоритми за оптимизация, които позволяват на машините да вземат решения в реално време. Това подобрява безопасността и енергийната ефективност, като намалява риска от скъпи грешки или аварии в производството.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Embedded learning and optimization for the next generation of smart industrial control systems

Digital technologies are transforming all sectors of our economy and will increasingly do so in the years to come. Thanks to the increasing capabilities of digital technologies, the next generation of smart industrial control systems (SICS) are expected to learn from streams of data and to take optimal decisions in real-time on the process at hand, leading to increased performance, safety, energy efficiency, and ultimately value creation. However, to develop this new control systems bottlenecks have to be overcome, the two most critical being: (1) the fact that the computation availability for industrial control systems is locally embedded in each system or subsystem, possibly with limited communication capabilities and distributed topologies; (2) the fact that industrial applications require reliable algorithms, with interpretable and verifiable behavior. Both these bottlenecks are related to safety aspects, which are crucial in applications where a single computation error can cause high economic and environmental cost or even damage to people. Numerical optimization is at the very core of both learning and decision-making, since both the extraction of information from data and the choice of the most suitable action are naturally cast as optimization problems and solved numerically. Therefore, the overall objectives of the project were to develop embedded learning- and optimization-based control methodologies for SICS, while training highly qualified and competent researchers. Over the course of the project, ELO-X has developed novel methods for model predictive control, learning-based control with robustness guarantees, and optimization algorithms for embedded and mixed-integer systems. These were implemented and demonstrated in real-world applications such as autonomous vehicles, hydraulic systems, robotic manipulators, and temperature control units. The project produced over 100 scientific publications, several high-TRL software tools, and six open-source packages. As of June 2025, five PhD theses have been successfully defended, with others nearing completion, and several ESRs have already transitioned into research positions across Europe. Visit https://elo-x.eu/ for more information on the ELO-X network.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Thanks to the increasing capabilities of digital technologies, the next generation of industrial control systems are expected to learn from streams of data and to take optimal decisions in real-time, leading to increased performance, safety, energy efficiency, and ultimately value creation.Numerical optimization is at the very core of both learning and decision-making, and machine learning algorithms and artificial intelligence raise huge worldwide research interest, often using cloud computing and large data centers for their optimization computations.However, in order to bring learning- and optimization-based automated decision-making into smart industrial control systems (SICS), two important bottlenecks have to be overcome: (1) computational resources on industrial control systems are locally embedded and limited, and (2) industrial control applications require reliable algorithms, with interpretable and verifiable behavior. Both requirements partially stem from safety aspects, which are crucial in applications where a single computation error can cause high economic and environmental cost or even damage to people.Pushing the performance boundary of SICS to leverage advanced digital technologies will therefore involve both fundamental new research questions and technological solutions, calling for a new set of advanced methods for embedded learning- and optimization-based control algorithms. Through its 15 PhD students hosted and seconded at 11 top European research centers (6 academic, 5 industrial) and 4 partner organizations in the US, Japan and China, ELO-X will address the timely and pressing need for highly qualified and competent researchers who will develop embedded learning- and optimization-based control methodologies for SICS, thus enabling new and possibly game-changing digital technologies for important EU industries.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз