Abstract
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 language | English |
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Publication status | Published - 2006 |
Event | International Control Conference (ICC2006) - Glasgow, United Kingdom Duration: 30 Aug 2006 → 1 Sept 2006 |
Conference
Conference | International Control Conference (ICC2006) |
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Country/Territory | United Kingdom |
City | Glasgow |
Period | 30/08/06 → 1/09/06 |