Generalized fuzzy environment models learned with genetic algorithms for a robotic force control

Impedance control allows the manipulator to change the mechanical impedance such as inertia, damping and stiffness, acting between the end-effector and its environment. However, to achieve stable force control under unknown stiff environments, complicated tuning of desired impedance parameters is needed. Among the parameters, the desired damping is the most significant to suppress overshoots and oscillations. In the paper generalized fuzzy environment models with anisotropy are proposed to systematically determine the desired damping against unknown environments. The models learned with genetic algorithms, can estimate each directional stiffness of the environment and yield the desired damping, considering the critical damping condition of the control system. Position and force control simulations are shown to demonstrate the effectiveness and promise of the models.