Muscle Synergy-Based Control of Human-Manipulator Interactions

Abstract

One challenge of designing robot-assisted control is to identify and estimate human intention force and motion under dynamic disturbances. We present a robotic control design to help human in manipulator-assisted upperlimb movement applications. The control design uses a muscle synergy-based neural network method to predict human force and intention motion. A disturbance observer-based controller is designed to eliminate the influence of disturbances and allows human operators to achieve task by using their normal efforts. The control design takes advantage of predicted human force and intention motion to provide proper assistance in the human-manipulator interactions. Human subject experiments are presented to demonstrate the control robustness and performance of disturbance rejection. The comparison results with a benchmark controller also confirm that the proposed design provides manipulator-assisted capability to save human effort when there are additional loads and unknown disturbances.