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Proceedings Paper

Payload-invariant servo control using artificial neural networks
Author(s): Mark E. Johnson; Michael B. Leahy; Steven K. Rogers
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Paper Abstract

A new form of adaptive model-based control is proposed and experimentally evaluated. An Adaptive Model-Based Neural Network Controller (AMBNNC) uses multilayer perceptron artificial neural networks to estimate the payload during high speed manipulator motion. The payload estimate adapts the feedforward compensator to umnodeled system dynamics and payload variations. The neural nets are trained through repetitive presentation of trajectory tracking error data. The AMBNNC was experimentally evaluated on the third link of a PUMA56O manipulator. Servo tracking performance was evaluated for a wide range of payload and trajectory conditions and compared to a non-adaptive model-based controller. The superior tracking accuracy of the AMBNNC demonstrates the potential of our proposed technique. 1.

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21184
Show Author Affiliations
Mark E. Johnson, U.S. Air Force Institute of Te (United States)
Michael B. Leahy, U.S. Air Force Institute of Te (United States)
Steven K. Rogers, U.S. Air Force Institute of Te (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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