Iterative learning identification and computed torque control of robots

This paper deals with a new iterative learning dynamic identification and control method of robot. The robot is closed-loop controlled with a Computed Torque Control (CTC). 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 off-line linear least squares estimated (IDIM-LS) where the IDIM is calculated with the joint position and its noisy derivatives, which cannot take into account variations of the parameters. 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. A study of convergence of the method is performed in simulation and an experimental setup with stationary parameters and with a variation of the payload on a prismatic joint validates the procedure.