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Modeling and compensation of frictional uncertainties in motion control: a neural network based approach

Frictional uncertainties are known to be a major cause of performance degradation in motion control systems. This paper investigates the modeling and compensation of nonlinear friction dynamics in direct drive servo mechanisms. Different modeling techniques such as, model based adaptive identification, modeling based on experimental data and neural network based approximation are discussed and experimentally tested on the first link of a direct drive manipulator.