This paper deals with a new iterative learning dynamic identification method of robot controlled with a Computed Torque Control (CTC) law. The parameters of the Inverse Dynamic Model (IDM), which calculates the CTC, are calculated to minimize the quadratic error between the actual joint force/torque and a joint force/torque calculated with the Inverse Dynamic Identification Model (IDIM), linear in relation to the parameters. Usually the parameters are estimated off-line and the IDIM is calculated with the joint position and the noisy derivative of it and cannot take into account variations of the parameters (IDIM-LS method). The new method called IDIM-ILIC (IDIM with Iterative Learning Identification and Control) overcomes these 2 drawbacks. The parameters are periodically calculated over a moving time window to update the IDM of the CTC, and the IDIM is calculated with the noise-free data of the trajectory generator, which avoids using the noisy derivatives of the actual joint position. An experimental setup on a prismatic joint validates the procedure with stationary parameters and with a variation of the payload.