Deep Learning-Based Trajectory Tracking Control forUnmanned Surface Vehicle

<table class="table-group" id="tab7"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Vehicle name</td><td class="align_center">Adjusted by DNN model</td><td class="align_center">Not adjusted</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">The average of level</td><td class="align_center">1.8</td><td class="align_center">2.8</td></tr><tr><td class="align_left">The variance of level</td><td class="align_center">1.36</td><td class="align_center">1.36</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Performance indices in waypoint behavior effect evaluation.</div>

Mathematical Problems in Engineering

tab7

Table 7

Table 7: Deep Learning-Based Trajectory Tracking Control forUnmanned Surface Vehicle 