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Decision & control

Measurements are the key to knowledge and this also applies to intelligent mechatronic systems. But it doesn't stop there. We are currently working on robust, self-learning measuring and knowledge tools to improve the performance level of mechatronic systems in all conditions.

Why this research?

Production environments face quite a few changes. They must start manufacturing in a highly adjustable way (lot size 1) at the lowest possible operational cost. Customers expect zero-defect products, no machine downtimes and absolute flexibility. We evolve from mass production to mass customisation.

Sensors play an important part in production environments. For instance, they can tell when a machine requires maintenance, monitor the product quality and calculate the efficiency of the production process. There are also ever more powerful, smaller and cheaper sensors available on the market.

But measuring is only the beginning. The collected data must be interpreted and, based on that, decisions must be taken to improve the system’s functioning. However, in modern production environments these decisions are becoming increasingly complex because one should not only consider a variable production process but also a changing production environment (for instance, the same machine operating in both Antwerp and Shanghai and exchanging data) and the (maintenance) status of the production plants.  

Only in this way, we will achieve high-performance production environments that are able to keep production activities in Flanders.  

Concrete research objectives

We contribute to the development of technology for intelligent monitoring, for advanced control and for supporting the decision-making process. We zoom in on the following research domains:

Sensing & monitoring

  • Glass fibre sensors: robust alternative for measuring, among others, pressure and temperature. 
  • Predictive condition monitoring, focusing on bearings, gears, motors and electromechanical powertrains.
  • Indoor and outdoor localisation systems: AGVs, mobile robot platforms and drones. These typically make use of a variety of technologies. Our research focuses on finding the most efficient solution for improved accuracy, a higher update rate and a better cost efficiency.
  • Vision-based monitoring: 1D, 3D and infrared.
  • Self-learning sensors, which automatically adjust to their environment so as to generate more reliable data, configure themselves and perform self-diagnostic tests on the occurrence of errors.

Control & decision making    

  • Optimal control systems thanks to new, robust operating algorithms that are easier to adjust and operate in real time so as to permanently ensure the highest possible quality.
  • Learning control, enabling the system to adjust to variable production processes and anticipate the demand for lot size 1.
  • Optimisation based on fleet data.
  • Supporting tools in the decision-making process that help to filter out relevant data.

For whom? 

  • Manufacturing companies looking for a strategy to obtain a zero-defect, zero-machine downtime production.
  • Manufacturing companies that need technology for developing flexible, automated production lines.
  • Machine builders that wish to incorporate condition and process monitoring.
  • Software companies that develop tools for designing and testing mechatronic systems.

Concerned core labs

  • DecisionS
  • EEDT-DC (UGent)
  • B-Phot (VUB)
  • DMMS-M en PMA-P (KULeuven)

Ongoing projects

  • ROFALC_ICON: Fast and robust learning control (01/03/2016-28/02/2018)          
  • VIBMON_ICON: Cost-effective vibroacoustic monitoring (01/10/2015-30/09/2017)         
  • EVIT_ICON: Experimental-virtual testing (01/07/2015-31/12/2017)        
  • LOCATOR_ICON: Localization system for accurate tracking and navigation for autonomous operation (01/11/2016-31/10/2018)
  • MONICON_ICON: Monitoring and control of laser melting processes (01/04/2017-31/03/2019)
  • AVCON_ICON: Avoidance of collision and obstacles in narrow lanes (01/11/2017-31/10/2019)
  • MODA_ICON: Model based data analytics (01/12/2017-30/11/2019)      
  • MULTISENSOR_ICON: Multi sensor design and validation (01/01/2018-31/12/2019)        
  • MOFORM_SBO: Model based force measurements (01/02/2016-01/02/2020)       
  • ROCSIS_SBO: Robust and optimal control of systems with interacting subsystems (01/03/2016-29/02/2020)      
  • COMBILASER_SBO: Combination of laser addtive, laser subtractive and other laser processes for improved functional part characteristics (01/02/2016-31/01/2020)              
  • MULTISYSLECO_SBO: Multi system learning control (01/04/2018-30/03/2022)   
  • Smart Maintenance: Proeftuin Smart Maintenance (01/01/2018-31/12/2020)       
  • Smart Connectivity: Proeftuin Smart Connectivity (01/01/2018-31/12/2020)  

Participating companies

  • Atlas Copco Airpower nv
  • Averna nv
  • Bekaert NV (Bekaert Engineering)
  • CNH Industrial Belgium nv
  • DANA Belgium nv
  • dotOcean nv
  • Laser Cladding Venture NV
  • LayerWise nv
  • Maintenance Partners Belgium nv
  • Materialise nv
  • Michel Van De Wiele nv
  • Nikon Metrology Europe NV
  • Octinion bvba
  • Picanol nv
  • Punch Powertrain nv
  • Siemens Industry Software nv
  • Televic Rail nv
  • Tenneco Automotive Europe bvba
  • The Kobi Company bvba
  • Triphase nv
  • VCST Industrial Products
  • ZF Wind Power Antwerpen nv


Andrei Bartic - Cluster Manager