A wet clutch is a device that transfers torque between two shafts via a hydraulic mechanism. Wet clutch control is key to achieve smooth and fast clutch engagements. Optimal control of a wet clutch is not trivial because of the complexity of the system due to nonlinearities, hybrid dynamics and changing dynamics over time due to changing temperatures and wear. Nowadays simple parametrized feedforward controllers are used in industry. The parameters of the control signal are tuned by hand and updated over time by an operator to account for the changing dynamics. Several solutions to the wet clutch control problem exist in the literature, however the structure of the solutions is fixed beforehand and they rely on expert knowledge of the system. Another challenging aspect of wet clutch control is model uncertainty, currently robustness of performance is considered by running a finite number of experiments, but no hard guarantees can be given. In this thesis a method is developed to automatically synthesize controllers for a wet clutch which are robust to model uncertainties. The method uses Genetic Programming (GP) to automatically synthesize controllers. Using GP controllers for a wet clutch can synthesize without fixing the structure beforehand and in a multi-objective way. Robustness is considered by optimizing the worst case performance. To be able to give a guarantee on the worst case performance of a controller a method, that is able to formally guarantee a lower bound on the worst case performance using reachability analysis, is developed. This method is to costly to incorporate into GP and instead a use cheap estimation of the worst performance in practice.
This method was able to find robust controllers which outperformed a hand tuned baseline controller, but in order to compare this method to other methods in literature, real life experiments are needed.