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• W2046317946 abstract "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 they converge. The model-based strategies seems most suited for an online application, because they are inherently more robust and require a shorter convergence time. The model-free strategies meanwhile seem most suited to offline calibration procedures for complex systems where heuristic tuning rules no longer suffice." @default.
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• W2046317946 date "2014-12-01" @default.
• W2046317946 modified "2023-09-28" @default.
• W2046317946 title "Model-based and model-free learning strategies for wet clutch control" @default.
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• W2046317946 doi "https://doi.org/10.1016/j.mechatronics.2014.03.006" @default.
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