Iterative Learning Control for the filling of wet clutches

This paper discusses the development of an advanced Iterative Learning Control (ILC) scheme for the filling of wet clutches. In the presented scheme, the appropriate actuator signal for a new clutch engagement is learned automatically based on the quality of previous engagements, such that time-consuming and cumbersome calibrations can be avoided. First, an ILC controller, which uses the position of the piston as control input, is developed and tested on a non-rotating clutch under well controlled conditions. Afterwards, a similar strategy is tested on a rotating set-up, where a pressure sensor is used as the input of the ILC controller. On a higher level, both the position and the pressure controller are extended with a second learning algorithm, that adapts the reference position/pressure to account for environmental changes which can not be learned by the low-level ILC controller. It is shown that a strong reduction of the transmitted torque level as well as a significant shortening of the engagement time can be achieved with the developed strategy, compared to traditional time-invariant control strategies.