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Proceedings Paper

Experimental tests of a model reference neural network controller on nonlinear servosystems
Author(s): R. Rees Fullmer; Suwat Kuntanapreeda; Robert W. Gunderson
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Paper Abstract

A control design technique known as the Model Reference Neural Network (MRNN) method has recently been developed. In this method, neural network controllers are trained so that the controlled system response mimics that of a desired reference model. Since the controller can be trained using experimental test data consisting of command and response state data, it is equally applicable to linear and nonlinear systems. The MRNN procedure was experimentally evaluated by applying it to several systems which demonstrated nonlinear behavior typically found in servosystems, including significant stick-slip friction, backlash, and positionally dependent gravitational torques. The performance of the MRNN was then compared to both PID and linear model reference controllers. Experimental results indicate that the accuracy of the MRNN controller typically equals or exceeds the linear model reference controllers.

Paper Details

Date Published: 2 March 1994
PDF: 12 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169988
Show Author Affiliations
R. Rees Fullmer, Utah State Univ. (United States)
Suwat Kuntanapreeda, Utah State Univ. (United States)
Robert W. Gunderson, Utah State Univ. (United States)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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