Optimal Feedback Control for Predicting Dynamic Stiffness During Arm Movement

Abstract

Knowledge of the central nervous system (CNS) that constrains dimensions of freedom to control a redundant system of the body would provide inspiration for the robotic engineering. We estimated limb stiffness in Japanese monkeys (Macaca fuscata) during arm reaching movements using a robotic manipulandum and carried out numerical simulations. The estimated joint stiffness showed a diphasic pattern, and the end-point stiffness ellipses were modulated during the movement in a characteristic manner. The pattern of limb stiffness was reproduced by the numerical simulation using a musculoskeletal arm model and an approximate optimal feedback control (OFC). Although the arm model has a redundant system with multiple dimensions of freedom, the OFC minimized the redundancy by enhancing the task-relevant cost function. We suggest that the CNS may control the body according to a similar OFC law, and the knowledge might be useful for developing human-machine systems.