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Using industrial robots for machining applications in flexible manufacturing
processes lacks a high accuracy. The main reason for the deviation is the
flexibility of the gearbox. Secondary Encoders (SE) as an additional, high precision
angle sensor offer a huge potential of detecting gearbox deviations. This paper
aims to use SE to reduce gearbox compliances with a feed forward, adaptive
neural control. The control network is trained with a second network for system
identification. The presented algorithm is capable of online application and optimizes
the robot accuracy in a nonlinear simulation.