Mitigation of displacement and acceleration responses of a three story benchmark
structure excited by seismic motions is pursued in this study. Multiple 20-kN
magnetorheological (MR) dampers are installed in the three-story benchmark structure
and managed by a global fuzzy logic controller to provide smart damping forces to the
benchmark structure. Two configurations of MR damper locations are considered to
display multiple-input, single-output and multiple-input, multiple-output control
capabilities. Characterization tests of each MR damper are performed in a laboratory to
enable the formulation of fuzzy inference models. Prediction of MR damper forces by
the fuzzy models shows sufficient agreement with experimental results.
A controlled-elitist multi-objective genetic algorithm is utilized to optimize a set
of fuzzy logic controllers with concurrent consideration to four structural response
metrics. The genetic algorithm is able to identify optimal passive cases for MR damper
operation, and then further improve their performance by intelligently modulating the
command voltage for concurrent reductions of displacement and acceleration responses.
An optimal controller is identified and validated through numerical simulation and fullscale
experimentation. Numerical and experimental results show that performance of the
controller algorithm is superior to optimal passive cases in 43% of investigated studies.
Furthermore, the state-space model of the benchmark structure that is used in
numerical simulations has been improved by a modified version of the same genetic
algorithm used in development of fuzzy logic controllers. Experimental validation shows
that the state-space model optimized by the genetic algorithm provides accurate
prediction of response of the benchmark structure to base excitation.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1088 |
| Date | 15 May 2009 |
| Creators | Shook, David Adam |
| Contributors | Roschke, Paul N. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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