|
|
Policy search reinforcement learning for automatic wet clutch engagement Host Publication: 15th International Conference on System Theory, Control and Computing - ICSTCC 2011 Authors: M. Gagliolo, K. Bert Van Vaerenbergh, A. Rodriguez, A. Nowé, S. Goossens, G. Pinte and W. Symens Publisher: IEEE Publication Date: Oct. 2011 ISBN: 978-1-4577-1173-2
Abstract: In most existing motion control algorithms, a reference trajectory
is tracked, based on a continuous measurement of the system's
response. In many industrial applications, however, it is either
not possible or too expensive to install sensors which measure the
system's output over the complete stroke: instead, the motion can only be
detected at certain discrete positions. The control objective in
these systems is often not to track a complete trajectory
accurately, but rather to achieve a given state at the
sensor locations (e.g. to pass by the sensor at a given time, or
with a given speed).
Model-based control strategies are not suited for the control of these systems,
due to the lack of sensor data. We are currently investigating the potential of
a non-model-based learning strategy, Reinforcement Learning (RL), in
dealing with this kind of discrete sensor information. Here,
we describe ongoing experiments with a wet clutch, which has to be engaged
smoothly yet quickly, without any feedback on piston position. External Link.
|
|
