Neural network based learning control of PUMA 560 manipulator

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

Neural network based learning control of PUMA 560 manipulator

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

Research output: Contribution to conference › Paper › peer-review

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
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)
Country/Territory	United Kingdom
City	Glasgow
Period	30/08/06 → 1/09/06

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Cite this

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title = "Neural network based learning control of PUMA 560 manipulator",

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.",

author = "Tzimopoulos, {E. S. C.} and Becerra, {V. M.}",

year = "2006",

language = "English",

note = "International Control Conference (ICC2006) ; Conference date: 30-08-2006 Through 01-09-2006",

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TY - CONF

T1 - Neural network based learning control of PUMA 560 manipulator

AU - Tzimopoulos, E. S. C.

AU - Becerra, V. M.

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

M3 - Paper

T2 - International Control Conference (ICC2006)

Y2 - 30 August 2006 through 1 September 2006

ER -

Neural network based learning control of PUMA 560 manipulator

Abstract

Conference

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