ISSA算法在加油机器人动力学参数辨识中的应用
Application of ISSA Algorithm in Dynamic Parameter Identification of Refueling Robots
娄宇轩 1倪艳光 1潘若鸣 2刘玉 2夏为丙3
作者信息
- 1. 河南科技大学 机电工程学院,河南洛阳 471003
- 2. 洛阳千歌机器人科技有限公司,河南洛阳 471000
- 3. 洛阳中科人工智能研究院有限公司,河南洛阳 471000
- 折叠
摘要
为了提高加油机器人动力学参数的辨识精度,提出了一种多策略改进的麻雀搜索算法(ISSA)用于加油机器人动力学参数辨识.改进的方法包括使用改进的Logistic映射初始化种群,增强种群多样性;动态调整比例系数,优化发现者和跟随者数量;在发现者位置更新公式中加入基于当前迭代次数的扰动因子并引入基于柯西变异和Tent扰动的新扰动策略,保持种群多样性并防止算法陷入局部最优.经QFB100协作臂参数辨识实验验证,改进的麻雀搜索算法在加油机器人动力学参数辨识中展现出优越的性能,为加油机器人的运动控制和自适应能力提供了有效的支持.
Abstract
A multi-strategy improved sparrow search algorithm(ISSA)is proposed to improve the accuracy of kinetic parameter identification for a refueling robot.The improved method includes initialising the population with an improved logistic mapping to enhance population diversity,dynamically adjusting the scaling factor to optimise the number of discoverers and followers,adding a perturbation factor based on the current number of iterations to the discoverer position update formula and introducing a new perturbation strategy based on the Cauchy variation and Tent perturbation to maintain population diversity and prevent the algorithm from falling into a local optimum.The improved sparrow search algorithm shows superior performance in the kinetic parameter identification of the refueling robot,which provides effective support for the motion control and adaptive capability of the refueling robot,as verified by the QFB100 collaborative arm parameter identification experiments.
关键词
加油机器人/激励轨迹优化/参数辨识/改进麻雀搜索算法Key words
refuelling robot/excitation trajectory optimisation/parameter identification/improved sparrow search algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据