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Copyright © 2016 Bayram Akdemir. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Linear control is widely used for any fluid or air flows in many automobile, robotics, and hydraulics applications. According to signal level, valve can be controlled linearly. But, for many valves, hydraulics or air is not easy to control proportionally because of flows dynamics. As a conventional solution, electronic driver has up and down limits. After manually settling up and down limits, control unit has proportional blind behavior between two points. This study offers a novel valve control method merging pulse width and amplitude modulation in the same structure. Proposed method uses low voltage AC signal to understand the valve position and uses pulse width modulation for power transfer to coil. DC level leads to controlling the valve and AC signal gives feedback related to core moving. Any amplitude demodulator gives core position as voltage. Control unit makes reconstruction using start and end points to obtain linearization at zero control signal and maximum control signal matched to minimum demodulated amplitude level. Proposed method includes self-learning abilities to keep controlling in hard environmental conditions such as dust, temperature, and corrosion. Thus, self-learning helps to provide precision control for hard conditions.

Details

Title
Novel Intelligent and Sensorless Proportional Valve Control with Self-Learning Ability
Author
Akdemir, Bayram
Publication year
2016
Publication date
2016
Publisher
John Wiley & Sons, Inc.
ISSN
1687725X
e-ISSN
16877268
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1809569821
Copyright
Copyright © 2016 Bayram Akdemir. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.