粒子灰狼优化算法的激励轨迹优化

Xiaolei Wu, Bin Li, Jin Wu, Yaqiao Zhu
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引用次数: 0

摘要

针对工业机器人惯性参数辨识中的激励轨迹设计问题,提出了一种分步辨识与粒子灰狼优化算法(PSOGWO)来优化激励轨迹参数的设计。首先,采用牛顿-欧拉递推法推导并建立了机器人最小惯性参数观测矩阵,并以观测矩阵条件数准则作为优化目标函数;其次,介绍了粒子灰狼优化算法(PSOGWO);最后,利用粒子灰狼优化算法(PSOGWO)对满足多约束条件的周期傅立叶级数进行优化设计,作为激励轨迹。实验结果表明,采用所提优化方法设计的激励轨迹能够充分激发机器人的动态特性,提高了参数辨识的抗噪声能力,为准确获取机器人的动态参数奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of the excitation trajectory of particle gray wolf optimization algorithm
Aiming at the excitation trajectory design in the identification of inertial parameters of industrial robots, this paper proposes a step-by-step identification and particle gray wolf optimisation algorithm (PSOGWO) to optimise the design of excitation trajectory parameters. First of all, the robot's minimum inertial parameter observation matrix is derived and established by Newton-Eura recursive method, and the observation matrix condition number criterion is used as the optimisation objective function; secondly, the particle gray wolf optimisation algorithm (PSOGWO) is introduced; finally, the periodic Fourier series that meets multi-constraint conditions is optimised and designed as the incentive trajectory using the particle gray wolf optimisation algorithm (PSOGWO). Experimental results show that the excitation trajectory designed with the proposed optimisation method can fully stimulate the dynamic characteristics of the robot, improve the anti-noise ability of parameter identification, and lay a foundation for accurately obtaining the dynamic parameters of the robot.
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