A neural network compensator for uncertainties of robotic manipulators

Shigeru Okuma, Akio Ishiguro, Takeshi Furuhashi, Yoshiki Uchikawa

Research output: Contribution to journalConference articlepeer-review

21 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3303-3307
Number of pages5
JournalProceedings of the IEEE Conference on Decision and Control
Publication statusPublished - 1990 Jan 1
Externally publishedYes
EventProceedings of the 29th IEEE Conference on Decision and Control Part 6 (of 6) - Honolulu, HI, USA
Duration: 1990 Dec 51990 Dec 7

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization


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