基于模糊神经网络的冗余度变几何桁架机器人自适应控制

ADAPTIVE CONTROL OF REDUNDANT VARIABLE GEOMETRY TRUSS MANIPULATORS BASED ON FUZZY NEURAL NETWORK

  • 摘要: 本文提出了一种基于模糊神经网络(FNN)的机器人位置自适应控制方法.利用模糊神经网络模型来辨识冗余度变几何桁架机器人的逆动力学模型,用常规反馈控制器完成外部干扰的补偿和闭环控制.并以四重四面体变几何桁架机器人为例进行仿真计算,表明该控制方法具有良好的轨迹跟踪精度和抗干扰能力.

     

    Abstract: An adaptive control scheme for redundant variable geometry truss manipulators is proposed, based on fuzzy neural network in this paper. The fuzzy neural network model is used to identify inverse dynamic model of redundant variable geometry truss manipulators, and conventional feedback controller is applied to compensation of external interference and close-loop control. The simulation calculation for a four-celled tetrahedron based variable geometry truss manipulator is give. The method is proved to have good track accuracy and anti-interference characteristics.

     

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