Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td align="left">Method</td><td align="center"> Mean value </td><td align="center"> Standard deviation </td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td align="left"> NBC </td><td align="center"> −0.0149 </td><td align="center"> 0.0894 </td></tr><tr><td align="left"> HOSM </td><td align="center"> −0.0223 </td><td align="center"> 0.0967 </td></tr><tr><td align="left"> IONC </td><td align="center"> 0.0034 </td><td align="center"> 0.0398 </td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Comparison between inverse optimal neural controller (IONC) with respect to Neural Backstepping Controller (NBC) and High Order Sliding Mode (HOSM) controller.

Mathematical Problems in Engineering

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Table 1

Table 1: Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots 