Enhancements of Nonlinear Substructuring Control for Shake Table Experiments on Severely Damaged Structures
Author(s): |
Ryuta Enokida
Koichi Kajiwara |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Structural Control and Health Monitoring, February 2023, v. 2023 |
Page(s): | 1-23 |
DOI: | 10.1155/2023/6648638 |
Abstract: |
This study introduces enhancements for nonlinear substructuring control (NLSC) to achieve simultaneous control of acceleration and displacement in shake table substructuring experiments with severely damaged structures. Although shake table control is greatly affected by a specimen placed on its top and its nonlinear characteristics, accurate control, even with nonlinear characteristics, is essential for experimental purposes. The NLSC was developed for a dynamically substructured system (DSS) scheme involving nonlinear substructures, and its first application to a shake table experiment was performed on a one-storey steel frame. In the experiment, NLSC realised simultaneous control of table acceleration and displacement with a slight nonlinear characteristic within the frame, although its performance degraded as the nonlinearity became stronger. To address this degradation issue, this study introduces two techniques for enhancing NLSC shake table substructuring experiments. One is a composite filtering technique to minimise noise-contaminating displacement data fed back to the table control so that the error feedback action in NLSC can be fully utilised. The other involves a new linear model that is assumed to be more highly damped than the actual system to enhance the stability robustness of NLSC against nonlinear characteristics. After a series of numerical examinations, this study experimentally examined the enhancements on actual shake table experiments using a steel frame. Using the NLSC with the enhancements, a substructuring experiment was successfully conducted; moreover, the NLSC realised simultaneous control of the table acceleration and displacement with nearly 100% accuracy, even with severe nonlinear characteristics. |
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data sheet - Reference-ID
10731259 - Published on:
21/06/2023 - Last updated on:
21/06/2023