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.
To realize this potential, embedded learning and optimization methods need to be developed, able to operate in industrial devices and to guarantee high safety standards.
Assembly of temperature control devices at TOOL-TEMP, one of ELO-X members. ELO-X will enable intelligent and energy-optimal operation of these devices.
Embedded learning and optimization
enable efficient industrial applications
able to satisfy timing and resource constraints.
Siemens virtual and physical vehicle platform for autonomous driving research, in particular testing and validating embedded MPC and learning control technologies.
ELO-X addresses the timely and pressing need for highly qualified and competent researchers, able to develop embedded learning- and optimization-based control methodologies for SICS, thus enabling new technologies and the next generation of digital industrial products and processes.
Atlas Copco oil-free compressor with integrated dryer. A group of these compressors is steered by an embedded central controller running embedded Model Predictive Control algorithms.
ELO-X is a Marie Curie Innovative Training Network (ITN) funded by the European Commission Horizon 2020 program. With 15 doctoral researchers working at 6 research universities and 5 international companies from 5 European countries, and further 4 partner organizations in China, Japan and the US, ELO-X will accelerate research and development in embedded learning and optimization, delivering new methods and applications.