Structured identification of multivariable modal systems
This work addresses the need for interpretable models in industrial systems for control and monitoring, but it appears incremental as it builds on existing identification methods.
The paper tackled the problem of estimating physically interpretable modal models for complex multivariable mechanical systems from frequency response data, and the result was a two-step structured identification algorithm that produced accurate, minimal-order models validated on a prototype wafer-stage system.
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.