A high-efficiency positioning error compensation method for a large parallel mechanism based on pose correction similarity

Abstract

Purpose Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time. Design/methodology/approach The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity. Findings Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds. Practical implications The proposed algorithm is more efficient while maintaining the accuracy of the error compensation. Originality/value This method can be used to quickly position the error compensation of a large parallel mechanism.