A two-level optimization based learning control strategy for wet clutches

This paper proposes a two-level control strategy for wet clutches. On the low level, the control signal is calculated by solving a constrained optimal control problem. On the high level, the measured responses are used to update the system models and constraints that are used in the optimization for the next control signal. In this way a learning algorithm is obtained, which is able to optimize the control signal during normal operation, despite its complex and time-varying dynamic behavior, and without requiring long calibrations or complex models. The performance and robustness of this control scheme are validated on an experimental test setup.