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

摘要

This paper proposes a two-level control strategy for wet clutches. On the low level, thecontrol 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 theoptimization for the next control signal. In this way a learning algorithm is obtained, which is ableto optimize the control signal during normal operation, despite its complex and time-varying dynamicbehavior, and without requiring long calibrations or complex models. The performance and robustnessof this control scheme are validated on an experimental test setup.