A filter-based pseudo-negative-stiffness (FPNS) control is proposed for seismic control of base-isolated structures. The control algorithm is designed to produce a negative stiffness friction damping force with a gradual change at velocity switches, so that it is potential to prevent structures from experiencing significant jerks especially under earthquakes rich of high-frequency components. The control algorithm requires information only on device's displacement. The effect of the control parameters on structural performance is studied and the optimal combination of control parameters is obtained with the consideration of control efficiency and the economy of control force. The superior performance of an active control system employing the FPNS control algorithm over that employing the conventional PNS control algorithm is verified. A semi-active control design, MC-FPNS, is developed to produce the control force of the FPNS control algorithm by MR dampers. The effectiveness and robustness of the MC-FPNS control system are investigated through numerical analysis of the base-isolated benchmark problem under earthquakes scaled to different intensity levels. The proposed MC-FPNS control system is shown to be effective to not only prevent the isolator from failure but also improve the isolation functionality for a variety of earthquakes with different frequency contents and intensity levels. Moreover, the MC-FPNS control system is capable of suppressing transference of high-frequency components of ground motions to the superstructure. Figure 8. Evaluation criteria for the sample semi-active, DPNS and MC-FPNS control systems under earthquake ground motions at full intensity level. The left line is for the case of FP-X and FN-Y; the right line is for the case of FN-X and FP-Y.Figure 9. Evaluation criteria for the sample semi-active, DPNS and MC-FPNS control systems under earthquake ground motions at 0.25 times of the full intensity level. The left line is for the case of FP-X and FN-Y; the right line is for the case of FN-X and FP-Y.
In this paper, we present a series of experimental and numerical studies on the performance and modeling of a developed magnetorheological gel (MRG) damper. A bi-directional shear-type damper was designed and fabricated. The MRG damper, which utilizes the gel’s high viscosity, can effectively alleviate the settlement problem inherent in magnetorheological fluid damper applications. Then, dynamic performance experiments were carried out to obtain the damping force with sinusoidal and random displacement excitations. Based on the test results, the forward model of the damper was established using a backpropagation neural network. A genetic algorithm was employed to optimize both the network structure parameters and the initial weight and bias values. Different forward models generated using different training datasets were validated and compared with the RBFNN model and Bouc-Wen model using different test datasets. The validation results indicate that the neural network-based forward model greatly outperforms the RBFNN model and Bouc-Wen model in terms of the estimation performance. The influence of the inputs at previous time has also been investigated. Finally, to generate the command current for controlling the damper, inverse neural network models with optimized structure parameters were established using different training datasets. Validation results with different test datasets indicate that, although the predicted current generated by the inverse models has many high-frequency components, it can still act as an effective damper controller, with the resulting damping force calculated using the predicted current coinciding well with the desired behavior.
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