Koen Classens

Maarten van der Hulst, Rodrigo A. González, Koen Classens et al.

Physically interpretable models are essential for next-generation industrial systems, as these representations enable effective control, support design validation, and provide a foundation for monitoring strategies. The aim of this paper is to develop a system identification framework for estimating modal models of complex multivariable mechanical systems from frequency response data. To achieve this, a two-step structured identification algorithm is presented, where an additive model is first estimated using a refined instrumental variable method and subsequently projected onto a modal form. The developed identification method provides accurate, physically-relevant, minimal-order models, for both generally-damped and proportionally damped modal systems. The effectiveness of the proposed method is demonstrated through experimental validation on a prototype wafer-stage system, which features a large number of spatially distributed actuators and sensors and exhibits complex flexible dynamics.

Koen Classens, Jeroen van de Wijdeven, Maurice Heemels et al.

Fault detection and isolation (FDI) systems are critical for modern mechatronic production equipment, as their continuous operation is heavily dependent on the ability to detect and isolate faults in a timely and efficient manner. The aim of this paper is to address closed-loop aspects for linear systems and enable the application of well-known nullspace-based FDI synthesis conditions to mechatronic systems subject to actuator and sensor faults. These tailored FDI synthesis conditions are applied to a large-scale prototype wafer stage, showcasing the proposed approach through real experiments, thereby underlining the usefulness of the derived synthesis conditions for a wide range of production machines and scientific instruments.