Description du projet
Se préparer aux systèmes de contrôle intelligents optimisés du futur
La demande pour les technologies de systèmes intelligents est en hausse dans la plupart des domaines, qu’il s’agisse du secteur privé comme du secteur public. L’industrie voit également l’essor des applications de technologies intelligentes, notamment en matière de systèmes intelligents de contrôle industriel. Malheureusement, en raison de la nature limitée et localement intégrée des ressources de calcul des systèmes de contrôle industriel, ainsi que de la nécessité de développer des algorithmes fiables présentant un comportement vérifiable et interprétable (solutions pour le moment absentes du marché), les systèmes intelligents de contrôle industriel sont incapables d’atteindre le niveau d’optimisation constaté dans d’autres domaines. Le projet ELO-X, financé par l’UE, entend résoudre ces problèmes en mettant sur pied une équipe de doctorants et d’organisations partenaires, dont l’objectif sera la recherche et le développement de solutions et de méthodologies visant à relever ces défis.
Objectif
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.
Champ scientifique
Programme(s)
Régime de financement
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)Coordinateur
79098 Freiburg
Allemagne