对称结构Stewart机构位置正解的改进粒子群算法  

Improved Particle Swarm Optimization for Forward Positional Analysis of Symmetrical Stewart Parallel Manipulators

作　　者：车林仙[1] 何兵[1] 易建[1] 陈长忆[2] 罗佑新[3] 

机构地区：[1]泸州职业技术学院机电工程研究所 [2]泸州职业技术学院电子与信息工程系 [3]湖南文理学院机械工程学院

出　　处：《农业机械学报》2008年第10期158-163,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：湖南省“十一五”重点建设学科资助项目(项目编号:湘教通[2006]180);泸州职业技术学院科研基金资助项目(项目编号:K-0502);泸州市科技计划项目(项目编号:200610);四川省高等教学改革工程人才培养质量和教学改革项目(项目编号:06511-254);四川省精品课程建设项目(项目编号:08-359-174)

摘　　要：根据杆长约束条件,建立了求6-DOF对称结构Stewart并联机器人机构位置正解的无约束优化模型。针对标准粒子群算法容易陷入局部极值、进化后期收敛速度慢等缺点,提出了一种基于差异度评价指标的改进粒子群算法——自适应变异粒子群算法。为克服随机算法不易求出并联机构全部位置正解的缺点,采用分层搜索自适应变异粒子群算法求并联机构位置正解中的优化问题。数值实例表明,对于对称结构Stewart并联机器人机构位置正解问题,改进粒子群算法能求出全部装配构型,且收敛速度较快、精度较高。The unconstrained optimization model for the forward positional analysis of a 6 -DOF symmetrical Stewart parallel manipulator, which based on the constrained length of the bars, was presented. The standard particle swarm optimization （SPSO） has some demerits, such as relapsing into local extremum and slow convergence velocity in the late evolutionary. The improved PSO, adaptive mutation PSO （AMPSO）, based on the new difference index, were proposed to overcome the demerits of the SPSO. Aimed at all forward positional solutions of parallel mechanisms were hard to obtain, stochastic algorithms were used to solve these solutions. Directed towards this weakness, the hierarchical search adaptive mutation PSO （HSAMPSO） was adopted to make the optimal problem for forward positional analysis of parallel mechanisms. Numerical results for the forward position analysis of the symmetrical Stewart parallel manipulator showed that the HSAMPSO could solve all assembly configurations, and possess the performances of rather quick convergence speed and high precision.

关 键 词：STEWART并联机构 位置正解 粒子群算法 自适应变异 分层搜索 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TH112[自动化与计算机技术—控制科学与工程]