Hydraulic System Modeling through Memory-based Learning
Abstract
Hydraulic machines used in a number of applications are highly non-linear systems. Besides the dynamic coupling between the different links, there are significant actuator non-linearities due to the inherent properties of the hydraulic system. Automation of such machines requires the robotic machine to be atleast as productive as a manually operated machine, which in turn make the case for performing tasks optimally with respect to an objective function (say) composed of a combination of time and fuel usage. Optimal path computation requires fast machine models in order to be practically usable. This work examines the use of memory-based learning in constructing the model of a 25-ton hydraulic excavator. The learned actuator model is used in conjunction with a linkage dynamic model to construct a complete excavator model which is much faster than a complete analytical model. Test results show that the approach effectively captures the interactions between the different actuators.
BibTeX
@conference{Krishna-1998-14772,author = {Murali Krishna and John Bares},
title = {Hydraulic System Modeling through Memory-based Learning},
booktitle = {Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {1998},
month = {October},
volume = {3},
pages = {1733 - 1738},
publisher = {IEEE},
}