Recently Received:


Control and data-driven modeling using Symbolic methods (CADUSY)

The CADUSY project aims at automated and low cost modeling and control for a wide range of industrial applications. By collaborating with industrial partners and users, we aim at accelerating the utilization of our results in the high-tech industry: wafer scanners (ASML), and in the mechatronic industry (FMTC). The modeling and synthesis tools we develop within this project will have a significant impact on

  • Shortening the development cycle of new products in these areas by drastically reducing the time from concept to prototype;
  • Reducing the production costs of consumer electromechanical systems by making affordable the use of non-linear control tools.

Collaborating partners: TU/e, TUDelft, ASML, National Instruments, Evolved Analytics, FMTC.

HTSM GRANT, 2015-19

Nanometer-accurate planar actuation system (NAPAS)

The NAPAS project aims at overcoming the fundamental limits of the dynamic accuracy of moving-magnet planar motors by developing a highly accurate and experi-mentally verified understanding of the dynamics of the coil-magnet interactions together with the development of a control system that, by exploiting this knowledge, capable of significantly improving high-speed positioning and bordering the one-billion-ratio objective.

Collaborating partners: TU/e (EE-EPE), ASML, Philips, Prodrive, Tecnotion, SKF, TNO.


Data-Driven Modeling of High Complexity Nonlinear Systems

This 2 years research collaborative project aims at the development of efficient identification approaches for nonlinear dynamical systems.

Collaborating universities: TU/e, CRAN, EMD.

[The Van Gogh Program encourages the exchange of Dutch and French researchers in all disciplines as part of a common research project that is to be carried out.]

National Priority Research Program Qatar, 2013-16

Data-driven Linear Parameter-Varying Model Learning and Control of Complex Process Systems

This 3 years research project aims at the development of a powerful  LPV model learning and control framework for process systems with primary applications in the oil/gas industry, catalytic reduction and crystallization based separation units.

Collaborating universities: TU/e, QU, UH.

Companies: Qatar gas processing center (GPC), Qatar Chemical Company ltd. (Q-Chem).

VENI grant, NWO-EW, 2012-15

Data-Driven Linear Parameter-Varying Modeling of Nonlinear Dynamical Systems

This 3 years individual research project aims at surmount ing the challenges of nonlinear system identification by establishing an innovative synergy between the Machine Learning (ML) and LPV frameworks to develop computationally efficient modeling approaches capable of supporting control synthesis.

[ The VENI Innovational Research Incentives Scheme is directed at individual researchers, who have recently gained their PhDs, to give them the opportunity to further develop their ideas.]


Participating in AUTOPROFiT
(EU FP7 project), 2010-2014

Participating in the EU-FP7 project: Advanced Autonomous Model-Based Operation of Industrial Process Systems (work package 2).

TUDelft Fellowship, 2010

Fellowship award of the Delft University of Technology of young researcher excellence.

RUBICON grant, NWO, 2010

The Rubicon program offers talented researchers who have completed their doctorates at a Dutch university in the past year the chance to gain experience at a top research institution outside the Netherlands.

Visegrad standard grant, 2004

Visegrad foundation offers support for researchers from the Visegrad countries (CZ,HU,PL,SK) for cross-boarder research cooperation.