机械臂时变问题的两种新型抗谐波归零神经网络

IF 5.5 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neurocomputing Pub Date : 2025-07-12 DOI:10.1016/j.neucom.2025.130930

Bing Zhang , Xinglong Chen , Yuhua Zheng , Shuai Li , Duc Truong Pham , Yao Mao

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引用次数: 0

摘要

本文提出了两种新的抗谐波干扰归零神经网络模型：已知频率抗谐波神经网络（KFHRZNN）和未知频率抗谐波神经网络（UFHRZNN）。这些模型旨在解决时变矩阵的伪逆问题和机器人机械臂的逆运动学问题。通过精确地计算谐波干扰的导数，他们显著地减轻了这些干扰，从而提高了机器人在高速、动态环境中的控制效率。研究阐明了KFHRZNN和UFHRZNN的设计原理、收敛特性和稳定性评估。数值模拟和物理实验验证了这些模型在解决机械臂时变问题方面的有效性和优势，突出了它们对谐波干扰的精度和鲁棒性。

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Two novel harmonic-resistant zeroing neural networks for time-varying problems in robotic manipulators

This paper presents two novel harmonically disturbance-resistant zeroing neural network (ZNN) models: the known frequency harmonic-resistant ZNN (KFHRZNN) and the unknown frequency harmonic-resistant ZNN (UFHRZNN). These models are designed to tackle the pseudoinverse of time-varying matrices and inverse kinematics challenges in robotic manipulators. By precisely accounting for the derivatives of harmonic disturbances, they significantly mitigate these interferences, thereby improving the control efficacy of robots in high-speed, dynamic settings. The study elucidates the design rationale, convergence characteristics, and stability assessments for both KFHRZNN and UFHRZNN. Numerical simulations and physical experiments validate the effectiveness and advantages of these models in resolving time-varying issues within robotic manipulators, highlighting their precision and robustness against harmonic disturbances.

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来源期刊

Neurocomputing 工程技术-计算机：人工智能

CiteScore

13.10

自引率

10.00%

发文量

1382

审稿时长

70 days

期刊介绍： Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.