Iterative optimization of the filling phase of wet clutches

This paper considers the control of wet clutches, and presents a two-level control strategy to learn and adapt the control signals during normal machine operation. With this approach it is possible to avoid the current practise of experimental calibrations, where regular recalibrations are needed to compensate for time-varying dynamics, e.g. due to wear and changes in oil temperature. On a low level, the developed controller determines the actuator signal by solving an optimal control problem before each engagement of the clutch. The models and constraints for this optimization problem are iteratively updated by a high-level controller, which consists of a recursive identification algorithm to model the system dynamics, and of an ILC-type algorithm to learn appropriate values for the constraints. The performance and robustness of this control scheme are validated on an experimental test setup.