Human Motor Computational Model Through Iterative Model Reference Adaptive Control

A computational model using mechanical impedance control in combination with an iterative model reference adaptive control is proposed to capture the hypothesised mechanisms in human motor control and the learning capacity that humans exhibit in adapting their movements to new and unstructured environments. The model uses an iterative learning control law to model human learning through repetitive processes. In this proposed framework, motion command is carried out without the need for inverse kinematics. Learning is performed without an explicit internal model of the body or of the environment. The resulting framework is simulated and compared to the experimental data, involving subjects performing a task in the face of specified disturbance forces. In this paper, a stable task is specifically addressed as the example. It is shown that the proposed framework produces a stable and accurate description of the general behaviour observed in the human motor adaptation.