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Semiparametric dynamic model identification for hyper-redundant manipulator based on iterative optimization and neural network compensation
Micro/Nano Technology and Fine Mechanics | 更新时间:2024-02-01
    • Semiparametric dynamic model identification for hyper-redundant manipulator based on iterative optimization and neural network compensation

    • 机械臂动力学模型辨识领域取得了重要突破。针对超冗余机械臂动力学模型的精确辨识问题,研究团队提出了一种创新的半参数动力学模型辨识方法。该方法结合迭代优化和神经网络补偿,有效提高了参数辨识的准确性和效率。通过遗传算法优化回归矩阵条件数生成激励轨迹,建立了关节非线性摩擦模型,进一步提升了模型的物理可行性。实验结果表明,与传统算法相比,该方法显著降低了关节辨识力矩残差的均方根值,验证了其有效性和优越性。这一研究不仅为超冗余机械臂的精确控制提供了有力支持,也为机械臂动力学模型辨识领域的研究开辟了新的方向。
    • Optics and Precision Engineering   Vol. 32, Issue 2, Pages: 193-207(2024)
    • DOI:10.37188/OPE.20243202.0193    

      CLC: TP394.1;TH691.9
    • Published:25 January 2024

      Received:02 June 2023

      Revised:24 July 2023

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  • ZHOU Yufei,LI Zhongcan,LI Yi,et al.Semiparametric dynamic model identification for hyper-redundant manipulator based on iterative optimization and neural network compensation[J].Optics and Precision Engineering,2024,32(02):193-207. DOI: 10.37188/OPE.20243202.0193.

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