Learning strategies for wet clutch control

This paper presents an overview of model-based (Iterative Learning Control, Model Predictive Control and Iterative Optimization) and non-model-based (Genetic-based Machine Learning and Reinforcement Learning) learning strategies for the control of wet clutches. Based on theoretical considerations and a validation on an experimental test bench containing wet clutches, the benefits and drawbacks of the different strategies are compared. Although after convergence a good engagement quality can be obtained by all strategies, only model-based strategies are suited for online applicability. The convergence time for non-model-based strategies is too long such that they can only be applied during an offline calibration phase.