A study of human hand position control learning-output feedback inverse model

The acquisition of an inverse-kinematic model is required for motor control in humans. With the direct inverse modeling method that is a conventional method, a sufficient inverse model cannot be obtained when the input and output correspondence of the target system is not one-to-one and is non-linear. The problem is seeking the inverse-kinematic model of the human arm, including a wrist, falls into this category. The authors propose an inverse model which has an output error feedback path, and determines the input for the target system by means of iterative improvement. Hand position feedback control of a multi-joint manipulator in working coordinates includes the non-linear gain of the joint angles, for example, the pseudo-inverse of the Jacobian matrix. The authors show that learning of the hand position feedback gain is possible with the output feedback inverse model