Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex environment with high-frequency disturbance. Thereby, to enhance tracking performance in a teleoperation system, only traditional DOB technique is insufficient. In this paper, for the purpose of constructing a feasible teleoperation scheme, we develop a novel controller that contains a variable gain scheme to deal with fast-time varying perturbation, whose gain is adjusted linearly according to human surface electromyographic signals collected from Myo wearable armband. In addition, for tracking the motion of operator’s arm, we derive five-joint-angle data of a moving human arm through two groups of quaternions generated from the armbands. Besides, the radial basis function neural networks and the disturbance observer-based control (DOBC) approaches are fused together into the proposed controller to compensate the unknown dynamics uncertainties of the slave robot as well as environmental perturbation. Experiments and simulations are conducted to demonstrated the effectiveness of the proposed strategy.

中文翻译：

---

干扰观察器增强型可变增益控制器，用于使用穿戴式臂章进行运动捕捉的机器人遥控操作

当外部或内部未知扰动随时间缓慢变化时，基于扰动观测器（DOB）的控制器在估计和补偿扰动方面表现良好。但是，在某种程度上，机器人操纵器通常在具有高频干扰的复杂环境中工作。因此，为了增强远程操作系统中的跟踪性能，仅传统的DOB技术是不够的。在本文中，为了构建可行的远程操作方案，我们开发了一种新型控制器，该控制器包含一个可变增益方案以应对快速时变扰动，其增益根据从Myo穿戴式臂章收集的人体表面肌电信号线性调整。另外，为了跟踪操作员手臂的运动，我们通过臂章产生的两组四元数推导出运动的人手臂的五关节角数据。此外，将径向基函数神经网络和基于干扰观测器的控制（DOBC）方法融合到所提出的控制器中，以补偿从属机器人的未知动力学不确定性以及环境扰动。进行实验和仿真以证明所提出策略的有效性。