角秒级定位精度气浮转台驱动参数调节方法研究

Research on drive parameter adjustment method for sub-arcsecond level positioning accuracy air-bearing rotary table

为实现气浮转台角秒级定位精度,基于20组驱动器经验参数及其定位精度数据,采用改进粒子群算法和BP(back propagation)神经网络对驱动器参数进行优化。该方法以粒子群优化神经网络为基础,通过混沌映射提高粒子位置随机性,引入莱维飞行策略防止局部最优;构建气浮转台进行驱动器参数优化对比试验,经验法整定后转台定位精度为±6.91″,优化整定后转台定位精度为±2.27″,提升67.15%;经验法整定后转台重复定位精度为±5.99″,优化整定后转台重复定位精度为±2.00″,提升66.61%,结果表明所提出的参数优化方法可以较好的提高定位精度。

To achieve arc-second level positioning accuracy for the air-floating turntable,an improved particle swarm optimization algorithm and back propagati(BP)on neural network are employed to optimize the drive parameters based on 20 sets of empirical parameters and their positioning accuracy data.This method is based on particle swarm-optimized neural networks,which enhance particle position randomness through chaotic mapping and introduce a Lévy flight strategy to prevent local optima.A pneumatic rotary table was constructed for a comparative experiment on drive parameter optimization.After empirical tuning,the positioning accuracy of the rotary table was±6.91″.With optimized tuning,the positioning accuracy improved to±2.27″,a 67.15%enhancement.Moreover,the repeatability accuracy improved from±5.99″with empirical tuning to±2.00″after optimization,marking a 66.61%improvement.These results demonstrate that the proposed parameter optimization method effectively enhances positioning accuracy.