Browsing by Subject "system identification"
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Item Control of a benchmark structure using GA-optimized fuzzy logic control(2009-05-15) Shook, David AdamMitigation 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.Item Statistical Estimation of Two-Body Hydrodynamic Properties Using System Identification(2010-01-14) Xie, ChenA basic understanding of the hydrodynamic response behavior of the two-body system is important for a wide variety of offshore operations. This is a complex problem and model tests can provide data that in turn can be used to retrieve key information concerning the response characteristics of such systems. The current study demonstrates that the analysis of these data using a combination of statistical tools and system identification techniques can efficiently recover the main hydrodynamic parameters useful in design. The computation of the statistical parameters, spectral densities and coherence functions provides an overview of the general response behavior of the system. The statistical analysis also guides the selection of the nonlinear terms that will be used in the reverse multi-input / single-output (R-MI/SO) system identification method in this study. With appropriate linear and nonlinear terms included in the equation of motion, the R-MISO technique is able to estimate the main hydrodynamic parameters that characterize the offshore system. In the past, the R-MISO method was primarily applied to single body systems, while in the current study a ship moored to a fixed barge was investigated. The formulation included frequency-dependant hydrodynamic parameters which were evaluated from the experimental measurements. Several issues specific to this extension were addressed including the computation load, the interpretation of the results and the validation of the model. Only the most important cross-coupling terms were chosen to be kept based on the estimation of their energy. It is shown that both the heading and the loading condition can influence system motion behavior and that the impact of the wave in the gap between the two vessels is important. The coherence was computed to verify goodness-of-fit of the model, the results were overall satisfying.