Experimental tests of a model reference neural network controller on nonlinear servosystems

R. Rees Fullmer; Suwat Kuntanapreeda; Robert W. Gunderson

doi:10.1117/12.169988

2 March 1994 Experimental tests of a model reference neural network controller on nonlinear servosystems

R. Rees Fullmer, Suwat Kuntanapreeda, Robert W. Gunderson

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169988
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

R. Rees Fullmer, Suwat Kuntanapreeda, and Robert W. Gunderson "Experimental tests of a model reference neural network controller on nonlinear servosystems", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169988

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