Supervisory model predictive impedance control for human arm movement

Abstract

Impedance control is described as the ability to modify characteristics of musculoskeletal impedance by the motor control system. This ability plays a significant role in posture control and fulfilling movements, in particular, at the presence of environmental disturbances. In addition, learning ability in human movement necessitates incorporating a type of model for environment and/or musculoskeletal system. In this study a fuzzy supervisory controller unit is suggested to coordinate impedance and model based control strategies. Results from computer simulations showed that both suitable impedance values and a proper internal model are required to fulfill movements similar to those of humans under different circumstances. This study showed that beside this modulation, the maximum motor learning may occur in direction with the least impedance and the most kinematic error. It also concluded that confronting abrupt changes in disturbance, the system managed to decrease error without learning the new dynamic using previous knowledge by supervisory system. A part of this compensation is due to stiffness variations and another part is due to decreasing the influence of model based controller.