Friction is an inevitable nonlinear phenomenon existing in servomechanisms. Friction errors often affect their motion and contour\naccuracies during the reversemotion. To reduce friction errors, a novel time-varying friction compensation method is proposed to\nsolve the problem that the traditional friction compensation methods hardly deal with. This problem leads to an unsatisfactory\nfriction compensation performance and the motion and contour accuracies cannot be maintained effectively. In this method,\na trapezoidal compensation pulse is adopted to compensate for the friction errors. A generalized regression neural network\nalgorithm is used to generate the optimal pulse amplitude function.The optimal pulse duration function and the pulse amplitude\nfunction can be established by the pulse characteristic parameter learning and then the optimal friction compensation pulse can\nbe generated. The feasibility of friction compensation method was verified on a high-precision X-Y worktable. The experimental\nresults indicated that the motion and contour accuracies were improved greatly with reduction of the friction errors, in different\nworking conditions. Moreover, the overall friction compensation performance indicators were decreased by more than 54% and\nthis friction compensation method can be implemented easily on most of servomechanisms in industry.
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