114 research outputs found
Artificial Intelligence based Position Detection for Hydraulic Cylinders using Scattering Parameters
Position detection of hydraulic cylinder pistons is crucial for numerous
industrial automation applications. A typical traditional method is to excite
electromagnetic waves in the cylinder structure and analytically solve the
piston position based on the scattering parameters measured by a sensor. The
core of this approach is a physical model that mathematically describes the
relationship between the measured scattering parameters and the targeted piston
position. However, this physical model has shortcomings in accuracy and
adaptability, especially in extreme conditions. To overcome this problem, we
propose Artificial Intelligence (AI)-based methods to learn the relationship
directly data-driven. As a result, all Artificial Neural Network (ANN) models
in this paper consistently outperform the physical one by a large margin. Given
the success of AI-based models for our task, we further deliberate the choice
of models based on domain knowledge and provide in-depth analyses combining
model performance with the physical characteristics. Specifically, we use
Convolutional Neural Network (CNN)s to discover local interactions of input
among adjacent frequencies, apply Complex-Valued Neural Network (CVNN) to
exploit the complex-valued nature of electromagnetic scattering parameters, and
introduce a novel technique named Frequency Encoding to add weighted frequency
information to the model input. By combining these three techniques, our best
performing model, a complex-valued CNN with Frequency Encoding, manages to
significantly reduce the test error to hardly 1/12 of the one given by the
traditional physical model.Comment: 16 pages, 10 figure
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