Research Article
Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications
Table 2
Prediction results of RBD, NDL, and SDL models.
| ā | Axis 1 | Axis 2 | Axis 3 | Axis 4 | Axis 5 | Axis 6 | All axes |
| Cross 1 | RBD | 2.2899 | 2.3417 | 2.3683 | 0.6523 | 0.5889 | 0.5290 | 1.7024 | NDL | 2.6107 | 2.7715 | 1.8227 | 0.3418 | 0.4487 | 0.4577 | 1.7486 | SDL | 1.8310 | 1.9460 | 1.3504 | 0.3450 | 0.4220 | 0.4524 | 1.2560 |
| Cross 2 | RBD | 2.3316 | 2.3162 | 2.2893 | 0.6642 | 0.6498 | 0.5525 | 1.6937 | NDL | 2.3342 | 2.5439 | 2.5428 | 0.3864 | 0.5162 | 0.4722 | 1.7807 | SDL | 1.6721 | 1.8492 | 1.4528 | 0.3435 | 0.4633 | 0.4654 | 1.2162 |
| Cross 3 | RBD | 2.4861 | 2.6723 | 2.2126 | 0.6417 | 0.5924 | 0.3582 | 1.7846 | NDL | 2.2477 | 2.4820 | 1.1689 | 0.4081 | 0.4487 | 0.3977 | 1.4779 | SDL | 1.4031 | 1.5533 | 0.9656 | 0.3294 | 0.4022 | 0.3428 | 0.9748 |
| Cross 4 | RBD | 2.7325 | 2.7323 | 2.5119 | 0.6625 | 0.6386 | 0.3708 | 1.9246 | NDL | 2.3239 | 2.1714 | 1.6374 | 0.3745 | 0.4245 | 0.4280 | 1.4889 | SDL | 1.4009 | 1.6816 | 1.1613 | 0.3435 | 0.4399 | 0.3696 | 1.0478 |
| Cross 5 | RBD | 2.8011 | 2.7946 | 2.2741 | 0.6326 | 0.5795 | 0.3620 | 1.9015 | NDL | 1.6768 | 2.2989 | 1.6690 | 0.4346 | 0.3893 | 0.3393 | 1.3746 | SDL | 1.5326 | 1.6847 | 1.0004 | 0.3385 | 0.3775 | 0.3058 | 1.0439 |
| Cross average | RBD | 2.5282 | 2.5714 | 2.3313 | 0.6506 | 0.6098 | 0.4345 | 1.8014 | NDL | 2.2387 | 2.4535 | 1.7682 | 0.3891 | 0.4455 | 0.4190 | 1.5741 | SDL | 1.5679 | 1.7430 | 1.1861 | 0.3400 | 0.4210 | 0.3872 | 1.1077 |
|
|