This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

中文翻译：

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带有干扰观测器的约束机器人机械臂的自适应模糊神经网络命令滤波阻抗控制


本文提出了一种自适应模糊神经网络 (NN) 命令滤波阻抗控制，用于带有扰动观测器的约束机器人操纵器。首先，引入势垒李亚普诺夫函数来处理全状态约束。其次，引入自适应模糊神经网络来处理未知的系统动力学，并设计扰动观测器来消除未知界扰动的影响。然后，设计了改进的辅助系统来抑制输入饱和效应。此外，采用指令滤波技术和误差补偿机制，直接获得虚拟控制律的导数，提高控制精度。利用势垒李亚普诺夫理论证明闭环系统中的所有信号都是半全局一致最终有界的。最后，进行仿真研究以说明所提出的控制方法的有效性。