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Nonlinear position estimators based on artificial neural networks for low costs manufacturing systems

Dogruer C., Kilic E., Dolen M., Koku B.

Journal of Automation Mobile Robotics and Intelligent Systems

2007

Vol. 1, No. 2

40-44

The accurate contral of CNC machine axis requires relatively expensive direct measurement sensors. In this paper, artificial neural network based position error estimators are comparatively evaluated as a part of a low-cost (but high performance) manufacturing system. Such schemes are very effective when the system is rot subjected to external loads as well as widely changing operating conditions such as ambient temperature.