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Abstract

In order to solve the problem of low accuracy of parameter identification in the dynamics model of the robotic arm, a dynamics parameter identification method based on the semiparametric dynamics model is proposed on the basis of designing and optimizing the joint motion trajectory. Firstly, a connecting rod rigid body dynamics model is established based on the Newton–Euler method, and a linear regression recombination model is derived to obtain the minimum inertia parameter set and optimize the excitation trajectory by combining with a genetic algorithm; Secondly, the parametric rigid body dynamics model is identified based on the improved chaotic particle swarm algorithm, and a moment compensation model is established based on the bi-directional long and short-term memory network to compensate for and model the error of the non-parameterization The model of moment compensation is established; again, the data are preprocessed by combining IIR Butterworth low-pass filtering and RLOESS method, and the accuracy of dynamics identification is verified by combining with experimental trajectories; finally, the parameter identification test shows that the model has higher accuracy and robustness compared with the traditional dynamics model.

Keywords

robotic arm kinetic model parameter identification improved chaotic particle swarm algorithm neural network

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Identification of robotic arm dynamics parameters based on semiparametric modeling

Abstract

Keywords

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References