Sensorimotor Control Using Adaptive Neuro-Fuzzy Inference for Human-Like Arm Movement

Abstract

In this study, a sensorimotor controller is designed to characterize the required muscle force to enable a robotics system to perform a human-like circular movement. When the appropriate muscle internal forces are chosen, the arm end-point tracks the desired path via joint-space feedback. An objective function of the least-change rate of muscle forces is determined to find suitable feedback gains. The parameter defining the muscle force is then treated as a learning parameter through an adaptive neuro-fuzzy inference system, incorporating the rate of change of muscle forces. In experimental section, the arm motion of healthy subjects is captured using the inertial measurement unit sensors, and then the image of the drawn path is processed. The inertial measurement unit sensors detect each segment motion’s orientation using quaternions, and the image is employed to identify the exact end-point position. Experimental data on arm movement are then utilized in the control parameter computation. The proposed brain–motor control mechanism enhances motion performance, resulting in a more human-like movement.