Dynamic compensation can improve the accuracy of trajectory tracking for industrial manipulators. For irregularly shape or flexible manipulators, however, it is difficult to measure the position of the center of mass (COM) so that its dynamic model cannot be expressed explicitly. This paper proposes a proportional derivative (PD) controller with radial basis function neural network based gravity and inertia compensation (RBFNN-GIC). The RBFNN is utilized to estimate the gravity disturbance and to enable identification of COM to calculate the compensated inertia term. The proposed strategy based on the dynamic model can be used on any robot arm whose COM, gravity and inertia are difficult to obtain. To demonstrate the optimization and effectiveness of proposed PD controller, comparative experiments between the proposed control scheme and the traditional data-fitting method least mean square method (LMS) are conducted on a 3 degree of freedom (DOF) robotic manipulator.

中文翻译：

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使用 RBF 神经网络对具有重力和惯性补偿的机械手进行局部放电控制

动态补偿可以提高工业机械手的轨迹跟踪精度。然而，对于不规则形状或柔性机械手，很难测量质心（COM）的位置，因此无法明确表达其动力学模型。本文提出了一种基于重力和惯性补偿的径向基函数神经网络的比例导数 (PD) 控制器 (RBFNN-GIC)。RBFNN 用于估计重力扰动，并能够识别 COM 以计算补偿惯性项。所提出的基于动力学模型的策略可用于任何难以获得 COM、重力和惯性的机器人手臂。为了证明所提出的 PD 控制器的优化和有效性，