A New Linear Motor Force Ripple Compensation Method Based on Inverse Model Iterative Learning and Robust Disturbance Observer

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left">Data</td><td class="align_center">Minimum</td><td class="align_center">Maximum</td><td class="align_center">Average</td><td class="align_center">Variance</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">Input (m)</td><td class="align_center">−0.1000</td><td class="align_center">0.1000</td><td class="align_center">0</td><td class="align_center">0.0033</td></tr><tr><td class="align_left">Output (N)</td><td class="align_center">−7.6685</td><td class="align_center">16.1804</td><td class="align_center">3.9452</td><td class="align_center">27.8808</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The statistics of cogging force data.</div>

Complexity

tab3

Table 3

Table 3: A New Linear Motor Force Ripple Compensation Method Based on Inverse Model Iterative Learning and Robust Disturbance Observer 