Experimental study on a hybrid-driven servo press using iterative learning control

Servo presses provide flexible punch motions, which satisfy different production needs. To achieve this merit, the control system must maintain the punch motion accurately despite versatile desired trajectories or varied loadings. Against this backdrop, the current paper proposes an iterative learning control (ILC) scheme for a hybrid-driven servo press. A proportional derivative (PD) type ILC controller that contains a closed-loop feedback controller is adopted. The sensitivity Jacobian is introduced into the controlling algorithm as the proportional gain in order to smoothen and increase the error convergence rate. The proposed ILC controller is then developed and verified on a servo press prototype. Experimental validations of a cup-shaped drawing are also carried out. The results show that the proposed ILC scheme effectively made the punch position root-mean-square (RMS) errors converge to less than 0.2 mm within five iterations. The precision was also improved to less than 50 μm that was equivalent to 35–40% of the original level without the ILC.