This paper proposes a novel control approach for a robot gripper in which the impedance control, fuzzy logic control, and iterative learning control are combined in the same control schema. The impedance control is used to keep the gripping force at the desired value. The fuzzy impedance controller is designed to estimate the best impedance parameters in real time when gripping unknown objects. The iterative learning control process is employed to optimize the sample dataset for designing the rule base to enhance the effectiveness of the fuzzy impedance controller. Besides, the real-time gripping force estimator is designed to keep an unknown object from sliding down when picking it up. The simulation and experiment are implemented to verify the proposed method. The comparison with another control method is also made by repeating the experiments under equivalent conditions. The results show the feasibility and superiority of the proposed method.

中文翻译：

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基于模糊规则库设计的梯度下降迭代学习控制的机械手最佳模糊阻抗控制

本文提出了一种新颖的机器人抓爪控制方法，该方法将阻抗控制，模糊逻辑控制和迭代学习控制组合在同一控制方案中。阻抗控制用于将夹持力保持在所需值。模糊阻抗控制器设计用于在抓取未知物体时实时估计最佳阻抗参数。迭代学习控制过程用于优化样本数据集，以设计规则库，以增强模糊阻抗控制器的有效性。此外，实时抓握力估算器旨在防止捡起未知物体时滑落。通过仿真和实验验证了该方法的有效性。通过在等效条件下重复实验，还可以与另一种控制方法进行比较。结果表明了该方法的可行性和优越性。