par Pinte, Gregory;Stoev, Julian;Symens, Wim;Dutta, Abhishek;Zhong, Yu;Wyns, Bart;De Keyser, Robin;Depraetere, Bruno;Swevers, Jan;Gagliolo, Matteo 
;Nowe, Ann
Référence 15th International Conference on System Theory, Control and Computing - ICSTCC 2011, IEEE, page (467-474)
Publication Publié, 2011
;Nowe, Ann Référence 15th International Conference on System Theory, Control and Computing - ICSTCC 2011, IEEE, page (467-474)
Publication Publié, 2011
Publication dans des actes
| Résumé : | 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. |
