The industrial robot reducer testing instrument dynamic torsional moment measurement error calibration, based on the Bisquare curve fitting–improved Bayes particle swarm optimization–nonlinear echo state network (BCF–IBPSO–NESN) method

Abstract

Industrial robots are important components in the production and manufacturing industry. As a core component of the industrial robot, the industrial robot reducer plays a crucial role in the performance of the entire industrial robot. The error analysis and accuracy traceability of the industrial robot reducer testing instrument are of great significance in improving the quality of the precision reducer. Therefore, it is essential to calibrate the dynamic torsional moment measurement error of the instrument. The features of the dynamic torsional moment measurement error are analyzed in this paper. Based on these features, a new dynamic torsional moment measurement error calibration method is proposed based on the Bisquare curve fitting–improved Bayes particle swarm optimization–nonlinear echo state network (BCF–IBPSO–NESN) algorithm. The proposed method focuses on calibrating the dynamic torsional moment measurement error of the industrial robot reducers in real time. The experimental results show that the dynamic torsional moment measurement error of the input side torsional moment measurement module and the output side torsional moment measurement module can be reduced to ±0.05 Nm and ±1 Nm, respectively. The contribution of this paper is that the method calibrates the dynamic torsional moment measurement error. It supplies a guideline for calibrating the dynamic torsional moment measurement error of the instrument under any load.