Modeling and Parameter Identification of MR Damper considering Excitation Characteristics and Current

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left">Parameter</td><td class="align_center">Α</td><td class="align_center"><i>β</i></td><td class="align_center"><i>δ</i></td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">Sensitivity (%)</td><td class="align_center">39.4325</td><td class="align_center">30.6834</td><td class="align_center">14.71123</td></tr><tr><td class="align_left">Parameter</td><td class="align_center"><i>F</i><sub>0</sub></td><td class="align_center"><i>c</i><sub>0</sub></td><td class="align_center"><i>k</i><sub>0</sub></td></tr><tr><td class="align_left">Sensitivity (%)</td><td class="align_center">0.3947</td><td class="align_center">14.7600</td><td class="align_center">0.0180</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The sensitivity results from the neural network model.</div>

Shock and Vibration

tab3

Table 3

Table 3: Modeling and Parameter Identification of MR Damper considering Excitation Characteristics and Current 