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Decision & control

Measurements are the key to knowledge. This also applies to intelligent vehicles, machines and production systems. But it doesn't stop there. In order to improve the performance level of these systems under all conditions, these measurements should also be used to robustly adapt their control.

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, absolute flexibility in customization and short delivery times. We’re evolving from mass production towards mass customisation.

Vehicles are becoming increasingly autonomous. This goes beyond self-driving cars, think of fruit picking robots, autonomously flying drones, automated guided vehicles in logistics, autonomous surveillance boats, etc.

Sensors play an important part in both production environments and autonomous vehicles. For instance, they can tell when a machine requires maintenance, monitor the product quality and calculate the efficiency of the production process. They determine the position and speed of a vehicle, distinguish between free and occupied space, differentiate between humans and machines, identify ripe fruits that need picking or pallet boards to be transported. Today, increasingly powerful, small and cheap sensors are being introduced onto the market, which continuously creates new opportunities.

But measuring is only the beginning. The collected data must be interpreted and, based on that, decisions must be taken to improve the production system and vehicle performance. However, in modern production environments these decisions are becoming increasingly complex because one should not only consider a variable production process but also a continuously changing production environment. Similarly, autonomous vehicles need to operate in complex, dynamically evolving environments. They need to adapt their path to avoid static and mobile obstacles, they must slow down near people but keep going when passing static obstacles.

Only by dedicated research in this field, we will achieve smart products (machines & vehicles) and 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 and advanced control systems 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. 
  • Self-calibrating accelerometers for inertial navigation
  • 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, 2D, 3D; visible and infrared), design of optical systems equipped with freeform lenses and image processing algorithms. 
  • Self-learning sensors, which automatically adjust to their environment so as to generate more reliable data, configure themselves and perform self-diagnostic tests upon the occurrence of errors.

Control & decision making    

  • Optimal control 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.
    • Context aware control: detection of actual operating context
    • Dynamic path planning: for mobile systems to avoid obstacles
    • Optimal trajectory generation: planning trajectory taking constraints into account
  • Learning control, enabling the system to adjust to variable production processes and anticipate the demand for lot size 1 production.
    • Safe learning: guarantees that constraints are respected during the learning process
    • Simple learning: increases robustness through use of simple, parametrized models
    • Shared learning: from the system's own experience or from that of similar systems in the fleet
  • Supporting tools in the decision-making process that help to filter out relevant data (using artificial intelligence).

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

Ongoing projects

Name Project Title


 MOFORM_SBO  Model based force measurements  01/02/2016   31/01/2020 
 COMBILASER_SBO    Combination of laser addtive, laser subtractive and other laser processes for improved functional part characteristics  01/02/2016   31/01/2020 
 ROCSIS_SBO    Robust and optimal control of systems with interacting subsystems  01/03/2016   29/02/2020 
 MONICON_ICON    Monitoring and control of laser melting processes  01/04/2017   31/03/2019 
 MODA_ICON    Model based data analytics  01/01/2018   31/12/2019 
 Smart Connectivity  Proeftuin Smart Connectivity  01/01/2018   31/12/2020 
 Smart Maintenance    Proeftuin Smart Maintenance  01/01/2018   31/03/2022 
 AVCON_ICON    Avoidance of collision and obstacles in narrow lanes  01/02/2018   31/01/2020 
 MULTISENSOR_ICON    Multi sensor design and validation  01/02/2018   31/01/2020 
 MULTISYSLECO_SBO    Multi system learning control  01/04/2018   31/03/2022 
 CONACON_ICON  Context Aware Control  01/04/2019  31/03/2021
 BEAMSHAPE_ICON   Optical beam shaping for high-productivity/quality laser-aided manufacturing  01/04/2019  31/03/2020
 PILS_SBO  Product inspection with little supervision  01/07/2019  30/06/2023
 HySLAM_SBO  A hybrid SLAM approach for autonomous mobile systems  01/08/2019  31/07/2023
 DGTwinPrediction_SBO  A digital twin for health monitoring and predictive maintenance    

Participating companies


Andrei Bartic - Cluster Manager