Project description
Preparing for the optimised smart industrial control systems of the future
Demand for smart system technologies has grown in most fields, both in the private and the public sectors. The industrial sector has also experienced a surge in smart technology applications as seen with smart industrial control systems. Unfortunately, due to the limited and locally embedded nature of computational resources on industrial control systems and the need for reliable algorithms with verifiable and interpretable behaviour, which currently are not present, smart industrial control systems cannot reach the level of optimisation seen in other fields. The EU-funded ELO-X project aims to combat these problems by assembling a team of PhD students and partner organisations that will research and develop solutions and methodologies for overcoming these difficulties.
Objective
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.
Fields of science
Programme(s)
Coordinator
79098 Freiburg
Germany