关键词: 并联机构, 误差分析, 误差补偿, RBF神经网络, 鲸鱼优化算法

Abstract: To address the issue of decreased accuracy in the block docking due to structural errors,the 4-PPPS parallel mechanism was focused.Initially,a closed-loop vector method was employed to establish a structural error model including 32 error terms.Subsequently,the influence of 16 measurable structural error parameters on the pose accuracy of the dynamic platform was specifically analyzed. It reveals that the length error of the moving pair along track direction has the greatest impact on the motion accuracy of the parallel mechanism,with a position error of 1.5 mm in the Z-axis direction when all four limbs exhibit such errors.Therefore,a compensation method based on the Whale Optimization Algorithm (WOA) optimized Radial Basis Function (RBF) neural network was proposed. By transforming pose errors into actuator length errors,it established a predictive model between theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters,it yielded the actuator length compensation to correct the actual pose of the dynamic platform. Simulation results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. Specifically,the mean errors in the X,Y,and Z directions are reduced from 0.169,0.188 and 0.159 mm to 0.002,0.001 and 0.003 mm,respectively. The maximum errors decrease from 0.208,0.231 and 0.195 mm to 0.012,0.001 and 0.019 mm,respectively. The average pose accuracy is improved by 85.07 %,demonstrating a significant compensation effect.

Key words: parallel mechanism, error analysis, error compensation, Radial Basis Function (RBF) neural network, Whale Optimization Algorithm (WOA)