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This paper presents an overview of model-based (Nonlinear Model Predictive Control, Iterative Learning Control and Iterative Optimization) and model-free (Genetic-based Machine Learning and Reinforcement Learning) learning strategies for the control of wet-clutches. The benefits and drawbacks of the different methodologies are discussed, and illustrated by an experimental validation on a test bench containing wet-clutches. In general, all strategies yield a good engagement quality once theydoi:10.1016/j.mechatronics.2014.03.006 fatcat:uzg6nfb5f5c4dcr2lsmp6duf7a