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Binrui Wang,Junwei Fang,Shunan Qi,Ling Wang,Xiaolong Liu,Haijun Ren 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.7
In order to solve the problem of lack of flexibility in direct teaching of industrial robots under complex working conditions, dynamic inertia term compensation is combined with the traditional force-free control algorithm to reduce the traction in the teaching process in this paper. Firstly, considering the multi-dimensional, strongly nonlinear and multi-parameter characteristics of the 6-DOF manipulator dynamic model, a step-by-step parameters identification method based on genetic algorithm is proposed. This method can effectively reduce the computational complexity and improve the optimization speed of industrial robot dynamic model identification. Secondly, based on the internal torque analysis of industrial robot joints, a forcefree compensation control algorithm based on the torque control is designed. Finally, the effectiveness of the step-by-step robot dynamic model parameter identification method is verified by a traction teaching experiment. In the verification experiment, the relative error rate between the predicted torque and the actual torque calculated from the identification results is 1.64 % to 5.99 %. After increasing the inertia term compensation, the traction force of the teaching process is reduced by 28.6 %. The experimental results show that the identification result is more accurate, and the proposed force-free control algorithm significantly improves the compliance of the teaching process.
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