Model-based Control and Identification of Motion Systems

Increasing performance requirements in motion systems necessitate taking the flexible, dynamic behaviour of these systems explicitly into account. New approaches are being investigated to exploit multiple additional actuators and sensors to actively compensate for system deformations. The increasing requirements justify an increased model dimensionality, complexity and accuracy, and associated control solutions. To enable this, we develop numerically reliable identification methods for complex systems with high-order dynamics and a large number of inputs and outputs. By connecting identification and robust control we can non-conservatively account for model uncertainty, and appropriately account for the system environment, such as by disturbance identification. New techniques based on identification and iterative learning control are being developed to accommodate reference-induced errors. Finally, the development of design principles for the mechanical design of high-tech systems, such as ultra-thin stages and adaptive optics systems,focuses on the research question of how to design for stiffness with high reproducibility and manufacturability, and sometimes for low cost and low thermal sensitivity. Applications are in the high-tech systems industry (ASML, Philips, Océ, FEI, various SMEs).

People active in this area: T.A.E. Oomen, M.F. Heertjes, A.G.d. Jager, L.F.P. Etman & M. Steinbuch

Ongoing PhD research projects

Completed PhD research projects