Neural network based learning control of PUMA 560 manipulator

E. S. C. Tzimopoulos, V. M. Becerra

    Research output: Contribution to conferencePaperpeer-review


    This paper investigates the practical application of neural networks with online learning for robot manipulator control. Experiments have been performed with a PUMA Unimate 560 industrial manipulator with reference signals of increasing complexity. The results presented demonstrate features of the proposed framework such as accurate trajectory tracking, online learning, and bounded weights, which guarantee closed loop stability.
    Original languageEnglish
    Publication statusPublished - 2006
    EventInternational Control Conference (ICC2006) - Glasgow, United Kingdom
    Duration: 30 Aug 20061 Sept 2006


    ConferenceInternational Control Conference (ICC2006)
    Country/TerritoryUnited Kingdom


    Dive into the research topics of 'Neural network based learning control of PUMA 560 manipulator'. Together they form a unique fingerprint.

    Cite this