Descripción del proyecto
Preparación para los sistemas inteligentes de control industrial optimizados del futuro
La demanda de tecnologías de sistemas inteligentes ha crecido en la mayoría de los campos, tanto en los sectores privados como públicos. El sector industrial también ha registrado un aumento del número de aplicaciones de tecnología inteligente, como se ha visto con los sistemas inteligentes de control industrial. Por desgracia, debido a la naturaleza limitada y arraigada a nivel local de los recursos informáticos de los sistemas de control industrial y a la necesidad de contar con algoritmos fiables con un comportamiento comprobable e interpretable, que actualmente no existen, los sistemas inteligentes de control industrial no pueden alcanzar el nivel de optimización observado en otros campos. El proyecto ELO-X, financiado con fondos europeos, tiene como objetivo combatir estos problemas reuniendo a un equipo de estudiantes de doctorado y organizaciones asociadas que investigarán y desarrollarán soluciones y metodologías para superar estas dificultades.
Objetivo
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.
Ámbito científico
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Régimen de financiación
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)Coordinador
79098 Freiburg
Alemania