Carnegie Mellon Robotics Institute
|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.
Number of pages: 6
|Murali Krishna and John Bares, "Hydraulic System Modeling through Memory-based Learning," IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 1998, pp. 1733-1738.|
author = "Murali Krishna and John Bares",
title = "Hydraulic System Modeling through Memory-based Learning",
booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems",
pages = "1733-1738",
publisher = "IEEE",
month = "October",
year = "1998",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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