|
|
Improving wet clutch engagement with Reinforcement Learning Host Publication: The 2012 International Joint Conference on Neural Networks Authors: K. Bert Van Vaerenbergh, M. Gagliolo, P. Vrancx, A. Nowé, J. Stoev, S. Goossens, G. Pinte and W. Symens Publisher: IEEE Publication Date: Jul. 2012 Number of Pages: 8 ISBN: 978-1-4673-1488-6
Abstract: A common approach when applying reinforcement learning to address control problems is that of first learning a policy based on an approximated model of the plant, whose behavior can be quickly and safely explored in simulation and then implementing the obtained policy to control the actual plant. Here we follow this approach to learn to engage a transmission clutch, with the aim of obtaining a rapid and smooth engagement, with a small torque loss. Using an approximated model of a wet clutch, which simulates a portion of the whole engagement, we first learn an open loop control signal, which is then transferred on the actual wet clutch, and improved by further learning with a different reward function, based on the actual torque loss observed.
|
|
