Model-based and model-free learning strategies for wet clutch control

Abhishek Dutta, Yu Zhong, Bruno Depraetere, Kevin Van Vaerenbergh, Clara Ionescu, Bart Wyns, Gregory Pinte, Ann Nowe, Jan Swevers, Robin De Keyser
2014 Mechatronics (Oxford)  
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
more » ... erge. 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.
doi:10.1016/j.mechatronics.2014.03.006 fatcat:uzg6nfb5f5c4dcr2lsmp6duf7a