On a selftuning decoupling controller for the joint control of a tendon driven multifingered robot gripper

This paper discusses couplings between the joints observed during position control using a tendon driven multifingered robot gripper. Depending on the mechanical construction these effects are non-linear and time variant as a consequence of friction and cable tension. Because the quality of the joint control is an important prerequisite for stable grasping, its design has to consider these coupling effects, generally using linear models. Therefore, the paper proposes a selftuning system which performs an iterative identification of a decoupling controller. The controller is stored in a CMAC-based neural network, so it can realize a non-linear decoupling. The authors present experimental results obtained by this method in comparison with a conventional approach