16th Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments
Multi-Rate Data Fusion Based Kalman Filtering with Unknown Input for Online Estimation of Dynamic Displacements
Publication: Earth and Space 2018: Engineering for Extreme Environments
ABSTRACT
Dynamic displacement is one of the most important measurements that describe the dynamic characteristics and safety of a structure. Measurement of dynamic displacement is also useful in structural control and structural identification. However, the effective estimation of dynamic displacement of structures is still a challenging task. To solve the difficulties and drawbacks of direct dynamic displacement monitoring. Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements has been proposed. Furthermore, some improved techniques for dynamic displacement estimation by fusing biased high-sampling rate acceleration and low-sampling rate displacement measurements have also been developed in recent years. However, the recent technique can only take the constant acceleration bias into account. In this paper, based on the recent Kalman filter with unknown input proposed by the authors, structural dynamic displacement is on line estimated based on multi-rate data fusion of biased high-sampling rate acceleration and low-sampling rate displacement measurements. The time history of acceleration bias is treated as ‘unknown input’ information to overcome the limitations of the previous technique. A numerical example is used to illustrate the proposed approach for on line estimation of structural dynamic displacement.
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ACKNOWLEDGMENTS
This research is financially supported by the Natural Science Foundation of China (NSFC) through the Grant No. 51678509.
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Published In
Earth and Space 2018: Engineering for Extreme Environments
Pages: 970 - 975
Editors: Ramesh B. Malla, Ph.D., University of Connecticut, Robert K. Goldberg, Ph.D., NASA Glenn Research Center, and Alaina Dickason Roberts
ISBN (Online): 978-0-7844-8189-9
Copyright
© 2018 American Society of Civil Engineers.
History
Published online: Nov 15, 2018
Published in print: Nov 15, 2018
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