宇航学报

宇航学报 ›› 2015, Vol. 36 ›› Issue (3): 278-283.doi: 10.3873/j.issn.1000-1328.2015.03.005

• 飞行器设计与力学 • 上一篇    下一篇

空间机器人惯性参数辨识的粒子群优化新算法

马欢，李文皓，肖歆昕，刘宏，蒋再男   

  1. 1. 中国科学院力学研究所，北京100190；2. 哈尔滨工业大学机器人技术与系统国家重点实验室，哈尔滨150001
  • 收稿日期:2014-01-24 修回日期:2014-04-10 出版日期:2015-03-15 发布日期:2015-03-25
  • 基金资助:

    国家重点基础研究发展计划（2013CB733000）

A New Particle Swarm Optimization Approach to the Inertia Parameters Identification of Onorbit Space Robot

MA Huan, LI Wen hao, XIAO Xin xin, LIU Hong, JIANG Zai nan   

  1. 1. Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190,China;
    2. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
  • Received:2014-01-24 Revised:2014-04-10 Online:2015-03-15 Published:2015-03-25

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摘要：

提出了针对一类多自由度空间机器人卫星惯性参数在轨辨识的一种粒子群（PSO）优化新算法。通过粒子邻域限定的多样性保持、低效粒子随机重置和粒子误差的序列性评价，得到了比常规方法更好的结果，且具有无附加燃料消耗、线动量测量和特定的机器人路径规划等便利性优点。仿真算例表明，该改进方法具有较高的准确性与效率。

关键词: 空间机器人, 参数辨识, 粒子群算法, 卫星

Abstract:

A new kind of particle swarm optimization (PSO) algorithm is proposed to identify the inertia parameters of an onorbit satellite equipped with a class of Multi-DOF robot. By diversity maintenance by limiting the definition of particle neighborhood, random reset of inefficient particles and sequential evaluation of particle errors, a better result is achieved in contrast with the classical PSO algorithm. Moreover, it doesn’t require additional fuel consumption, linear momentum measurement nor specific robot path planning. The simulation experiments show that the improved algorithm performs more accurately and efficiently.

Key words: Space robot；Parameter identification；Particles swarm optimization , Satellite

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