Evaluation of recursive Bayesian filters for modal contribution estimation in high-tech compliant mechanisms
P. E. de Bruin (Student TU Delft)
M.B. Kaczmarek (TU Delft - Mechatronic Systems Design)
Manon Kok (TU Delft - Team Manon Kok)
S. Hassan Hassan HosseinNia (TU Delft - Mechatronic Systems Design)
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Abstract
This study evaluates three recursive Bayesian input and state estimation algorithms, as introduced in the field of Structural Health Monitoring, for estimating modal contributions for high-tech compliant mechanisms. The aim of estimating modal contributions is the use for active vibration control. High-tech compliant motion stages allow for different sensor configurations, making new and interesting performance evaluations of these filters possible. The algorithms used, namely, the Augmented Kalman Filter (AKF), Dual Kalman Filter (DKF) and Gilijns de Moor Filter (GDF) are implemented on a compliant motion stage for guidance flexure deformation estimation. Our results show the GDF performs overall best, with good estimation performance and real-world tuning capability.
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