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Model-based and model-free learning strategies for wet clutch control This publication appears in: Mechatronics Authors: A. Dutta, Y. Zhong, B. Depraetere, K. Bert Van Vaerenbergh, C. Ionescu, B. Wyns, G. Pinte, A. Nowé and J. Swevers Volume: 24 Issue: 8 Pages: 1008-1020 Publication Year: 2014
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.
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