Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control

<table class="table-group" id="tab8"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Controller</td><td class="align_center">Sinusoidal signal (mm)</td><td class="align_center">Trapezium signal (mm)</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">PID</td><td class="align_center">0.2671</td><td class="align_center">0.2731</td></tr><tr><td class="align_left">ANNC</td><td class="align_center">0.2415</td><td class="align_center">0.2436</td></tr><tr><td class="align_left">IANNC</td><td class="align_center">0.0237</td><td class="align_center">0.0206</td></tr><tr><td class="align_left">IADNNC</td><td class="align_center">0.0084</td><td class="align_center">0.0092</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Comparison of the experimental results for RMSE.</div>

Mathematical Problems in Engineering

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Table 8

Table 8: Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control 

Table 8 | Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control 