This paper describes an impedance controller that uses the fast terminal sliding mode and adaptive neural networks. The proposed controller improves the dynamic trajectory performance and force tracking accuracy of robotic manipulators in three-dimensional uncertain environments. The key idea of the controller is that the improved adaptive nonsingular fast terminal sliding mode (ANFTSMC) uses integral control to eliminates chattering without affecting the tracking performance. Also, adaptive neural networks are introduced into the sliding mode controller to provide a local approximation in the dynamic model of the robotic manipulator, and the stability is guaranteed through the Lyapunov theory. Finally, combined with impedance control, the operation of the robotic manipulator is simulated in environments containing various obstacle types, and experimental validation has demonstrated the effectiveness and superiority of the proposed control scheme.

本文描述了一种使用快速终端滑模和自适应神经网络的阻抗控制器。所提出的控制器提高了机器人在三维不确定环境中的动态轨迹性能和力跟踪精度。该控制器的关键思想是改进的自适应非奇异快速终端滑模（ANFTSMC）使用积分控制来消除抖动而不影响跟踪性能。此外，将自适应神经网络引入滑模控制器，以在机器人机械手的动态模型中提供局部近似，并通过李雅普诺夫理论保证稳定性。最后，结合阻抗控制，模拟机器人在包含各种障碍物类型的环境中的操作，