烟台大学学报（自然科学与工程版）2026，Vol.39Issue(2)：199-207,9.DOI:10.13951/j.cnki.37-1213/n.251009
基于改进遗传算法的六自由度并联平台动力学参数辨识
Dynamic Parameter Identification of Six Degree-of-Freedom Parallel Platform Based on Improved Genetic Algorithm
摘要
Abstract
To improve the problems of premature convergence and high proportion of invalid solutions in traditional genetic algorithms for dynamic parameter identification of six degree-of-freedom parallel platforms,this study propo-ses an improved genetic algorithm incorporating dynamic priors.The algorithm first generates an initial population based on chaotic sequences,stratifies parameter sensitivity using the mRMR criterion,and filters valid solutions by combining parameter boundaries with physical constraints.Subpopulations are then divided based on cosine similar-ity,and collaborative evolution is achieved through a cyclic-chain dynamic competition mechanism,coupling-aware adaptive crossover,Cauchy mutation,and elitism.Finally,optimal identification parameters are obtained through iterative closed-loop optimization.Experimental results show that the algorithm reduces the average parameter error from 17.59%in traditional genetic algorithms to 3.69%,with all parameter errors controlled below 6.83%.Moreo-ver,the convergence time is improved from 0.9 s to about 0.03 s,ensuring rapid stabilization and a significantly improved convergence rate,thereby providing reliable parameter support for high-precision platform control.关键词
六自由度并联平台/遗传算法/两级物理约束/动态竞争/动力学参数辨识Key words
6-DOF parallel platform/genetic algorithm/two-level physical constraints/dynamic competition/dynamic parameter identification分类
信息技术与安全科学引用本文复制引用
边磊,刘唐英,王瑞乾,蔡树向..基于改进遗传算法的六自由度并联平台动力学参数辨识[J].烟台大学学报（自然科学与工程版）,2026,39(2):199-207,9.基金项目
国家自然科学基金资助项目(62503411). （62503411）
