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Carnegie Mellon Robotics Institute

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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.

Notes

Number of pages: 6

Text Reference

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.

BibTeX Reference

@inproceedings{Krishna_1998_2609,
   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",
}

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