Head stabilization based on a feedback error learning in a humanoid robot

In this work we propose an adaptive model for the head stabilization based on a feedback error learning (FEL). This model is capable to overcome the delays caused by the head motor system and adapts itself to the dynamics of the head motion. It has been designed to track an arbitrary reference orientation for the head in space and reject the disturbance caused by trunk motion. For efficient error learning we use the recursive least square algorithm (RLS), a Newton-like method which guarantees very fast convergence. Moreover, we implement a neural network to compute the rotational part of the head inverse kinematics. Verification of the proposed control is conducted through experiments with Matlab SIMULINK and a humanoid robot SABIAN.