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Research Papers

Auto-calibration Based Control and Its Application to a Kind of Electrohydraulic Poppet Valves

[+] Author and Article Information
Patrick Opdenbosch1

Sr. Research Engineer, Caterpillar Inc., Mossville, IL 61552opdenbosch_patrick@cat.com

Nader Sadegh

Associate Professor, George W. Woodruff School of Mechanical Engineering,  Georgia Institute of Technology, Atlanta, GA 30332nader.sadegh@me.gatech.edu

Wayne Book

HUSCO/Ramirez Distinguished Professor in Fluid Power and Motion Control, George W. Woodruff School of Mechanical Engineering,  Georgia Institute of Technology, Atlanta, GA 30332wayne.book@me.gatech.edu

According to the manufacturer, overall variability in conductance characteristics can be reduced to −10 mA/+30 mA by design improvements for the EHPV which are not considered herein.

This is the reason for calling this method input matching.

The EPHV’s are illustrated as proportional two-port two-position bidirectional hydraulic valves for the sake of simplicity.

Temperature effects are thus ignored.

1 LPH = 1 L/h = 2.78 × 10−7 m3 /s and 1 MPa = 1 × 106 MPa.

It is worth noting that learning of the low current region can also be accomplished. However, in this setup, low current meant that the EHPV is nearly closed causing the supply pressure to be high. The experiment was designed to stay away from this scenario since under these circumstances, the hydromechanical pressure cutoff controller in the pump acts to limit the supply flow.

1

Corresponding author.

J. Dyn. Sys., Meas., Control 133(6), 061004 (Sep 29, 2011) (10 pages) doi:10.1115/1.4004057 History: Received November 25, 2009; Revised January 22, 2011; Published September 29, 2011; Online September 29, 2011

This paper describes a novel learning/adaptive state trajectory control method and its application to electronic hydraulic pressure control. The control algorithm presented herein learns the inverse input-state mapping of the plant at the same time this map is employed in the feedforward loop to force the state of the plant to asymptotically converge to a prescribed state trajectory. The algorithm accomplishes this task without requiring prior exact information about the state transition map of the plant. The novel controller is applied to an electrohydraulic poppet valve with the objective of tracking a desired supply pressure signal. In this application, the controller learns the inverse conductance characteristics of the valve. The supply pressure tracking performance subject to the proposed controller is validated through experimental data.

FIGURES IN THIS ARTICLE
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Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 2

Hydraulic test-bed used in the pressure control application

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Figure 3

Transformation of the input space of the NLPN

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Figure 4

Flow conductance response without learning when y·=0

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Figure 5

Supply pressure response without learning when y·=0

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Figure 6

Flow conductance response with learning when y·=0

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Figure 7

Supply pressure response with learning when y·=0

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Figure 8

Flow conductance response with learning when y·≠0

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Figure 1

Diagram of the NBIM control law

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Figure 9

Supply pressure response with learning when y·≠0

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Figure 10

Inverse input-state mappings for the EHPV at steady state

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