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- W2891383218 abstract "An experimental comparison of two feed-forward based friction compensation methods is presented. The first method is based on the LuGre friction model, using identified friction model parameters, and the second method is based on B-spline network, where the network weights are learned from experiments. The methods are evaluated and compared via experiments using a six axis industrial robot carrying out circular movements of different radii. The experiments show that the learning-based friction compensation gives an error reduction of the same magnitude as for the LuGre-based friction compensation." @default.
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- W2891383218 date "2018-01-01" @default.
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- W2891383218 title "A Learning Approach for Feed-Forward Friction Compensation" @default.
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- W2891383218 doi "https://doi.org/10.1016/j.ifacol.2018.11.578" @default.
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