The authors propose neural networks which do not learn inverse dynamic models but compensate nonlinearities of robotic manipulators with the computed torque method. A comparison of the performance of these networks with that of the conventional adaptive scheme in compensating the unmodeled effects was carried out. As a result, the adaptive capability of the neural network controller with respect to the unstructured effects is shown, although the conventional scheme had no capability to reduce the unmodeled effects. Furthermore, a learning method of the neural network compensator with true teaching signals is shown. The tracking error of the robotic manipulator was greatly reduced.
|Number of pages||5|
|Journal||Proceedings of the IEEE Conference on Decision and Control|
|Publication status||Published - 1990|
|Event||Proceedings of the 29th IEEE Conference on Decision and Control Part 6 (of 6) - Honolulu, HI, USA|
Duration: 1990 Dec 5 → 1990 Dec 7