Abstract
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.
Highlights
Robotic manipulators are increasingly used in welding automation, robotic surgery [1], and space, as they are able to complete diverse tasks in complex environments, such as uncertain system dynamics, time-vary delays, and unknown external disturbances. e robot manipulator may work within dangerous environments for unfriendly tasks, such as handing radioactive material and searching, and the teleoperation control of the robot manipulator has been widely utilized into the controller design
The control framework we proposed in general is useful for obtaining the human-like characteristics, as well as simulating the control strategy in the robot manipulator system, and be of
We developed an adaptive control framework that fully incorporates a robot manipulator system with an EMGdriven NMS model and use it to control the motion of the robot manipulator with human-like characteristics
Summary
Robotic manipulators are increasingly used in welding automation, robotic surgery [1], and space, as they are able to complete diverse tasks in complex environments, such as uncertain system dynamics, time-vary delays, and unknown external disturbances. e robot manipulator may work within dangerous environments for unfriendly tasks, such as handing radioactive material and searching, and the teleoperation control of the robot manipulator has been widely utilized into the controller design. Yang et al [2] proposed an admittance-adaptation-based methodology for robot manipulators when interacting with unknown environments and guaranteed trajectory tracking performance. The robotic manipulator is teleoperated by the user with human-like characteristics. A human-like learning controller was proposed to optimally adapt interaction with unknown environments [4], and the human-like adaptivity was shown well by the robot manipulator in stable and unstable tasks. E development of robot manipulator controller with human-like characteristics (the user’s intention) requires accurate and robust decoding of motor function. EMG-based modelling methodologies have been utilized into various human-machine control algorithms for robot manipulators. Ryu et al [7] developed a continuous position-based strategy for a robot manipulator with EMG signals and the manipulator could replicate the movements from the user well, thereby improving the control strategy
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