Iterative Learning Control for wet-plate clutches

A wet-plate clutch (Fig. 1) is a mechanical device that transmits torque from its input axis to its output axis by means of friction. The input axis of the clutch is connected to a drum, which is a hollow cylinder with grooves on the inside. A first set of friction plates (clutch plates) with external toothing that can slide in those grooves are pressed against a second set of friction plates (clutch discs) with internal toothing that can slide over a grooved bus connected to the output axis. When the two sets of friction plates are pressed against each other by a piston, torque is transmitted. Initially, when the clutch is not engaged, the piston should be positioned as far as possible from the friction plates to avoid losses due to viscous friction of the oil between the plates. When the vehicle control unit gives the command to close the clutch, the distance between the piston and the plates should be decreased as fast as possible to zero, without the piston and the plates making brutal contact. Nowadays, this is achieved using feedforward control of the electro-hydraulic valve. However, long calibration procedures are necessary to find the optimal feedforward signal for a smooth clutch engagement. Furthermore, since the controlled system is time-varying (wear of the friction plates, variable temperature,...), regular recalibrations of the control signal are inevitable. To avoid these cumbersome calibrations, an ILC control system [1] is developed, which learns the appropriate control signal based on the quality of the previous engagements.