Robot manipulators have been extensively used in complex environments to complete diverse tasks. The teleoperation control based on human-like adaptivity in the robot manipulator is a growing and challenging field. This paper developed a disturbance-observer-based fuzzy control framework for a robot manipulator using an electromyography- (EMG-) driven neuromusculoskeletal (NMS) model. The motion intention (desired torque) was estimated by the EMG-driven NMS model with EMG signals and joint angles from the user. The desired torque was transmitted into the desired velocity for the robot manipulator system through an admittance filter. In the robot manipulator system, a fuzzy logic system, utilizing an integral Lyapunov function, was applied for robot manipulator systems subject to model uncertainties and external disturbances. To compensate for the external disturbances, fuzzy approximation errors, and nonlinear dynamics, a disturbance observer was integrated into the controller. The developed control algorithm was validated with a 2-DOFs robot manipulator in simulation. The results indicate the proposed control framework is effective and crucial for the applications in robot manipulator control.

中文翻译：

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使用基于肌电图驱动的神经肌肉骨骼模型的基于干扰观测器的机器人机械手模糊控制

机器人操纵器已在复杂环境中广泛使用，以完成各种任务。机器人操纵器中基于类人适应性的遥操作控制是一个不断发展且充满挑战的领域。本文使用肌电图（EMG）驱动的神经肌肉骨骼（NMS）模型为机器人操纵器开发了基于干扰观察者的模糊控制框架。运动意图（所需转矩）由EMG驱动的NMS模型根据用户的EMG信号和关节角度估算得出。所需的扭矩通过导纳过滤器传输到机械手系统的所需速度。在机器人操纵器系统中，将模糊逻辑系统（利用积分Lyapunov函数）应用于受模型不确定性和外部干扰影响的机器人操纵器系统。为了补偿外部干扰，模糊逼近误差和非线性动力学，将干扰观测器集成到控制器中。仿真中，开发的控制算法已通过2自由度机器人操纵器进行了验证。结果表明，所提出的控制框架对于机器人操纵器控制中的应用是有效和至关重要的。