A Novel Method of Extending the Linearity Range of Linear Variable Differential Transformer Using Artificial Neural Network

This paper proposes a simple and novel method of designing and developing high-linearity linear variable differential transformer (LVDT)-based displacement sensing systems. Conventionally, precise adjustment of windings is made to enhance the linearity range of LVDTs. The tedious job of pitch adjustment of windings of LVDTs can be overcome by using the proposed method. A functional link artificial neural network has been successfully used in this paper for nonlinear compensation of the LVDT. The effectiveness of the proposed method is demonstrated through computer simulation with the experimental data of two different LVDT. The complete algorithm with the practical setup for the development of a linear LVDT is presented in this paper.

I. Introduction

Linear variable differential transformers (LVDT) play a very vital role in all fields of engineering and industries for the purpose of measuring displacement, pressure, force, level, flow, and other physical quantities [1]. The performance of the control system depends upon the performance of the sensing elements. Many sensing elements are exposed to different types of environments during the course of measurement. Generally, instruments or sensors exhibit inherent nonlinear input–output characteristics. The performance of a sensor is also adversely affected by variations in excitation quantities and changes in ambient and winding temperatures. Due to such nonlinearities, a direct digital readout is not possible. As a result, the LVDT is used only in the linear region of its characteristics. in other words, its usable range gets restricted due to the presence of nonlinearity. Therefore, the accuracy of measurement is affected if the full range of the LVDT is used. Much effort has been made by many researchers to increase the range of linearity of LVDT. The performance of the LVDT is highly influenced by transducer geometry, arrangement of primary and secondary windings, quality of core material, variations in excitation current and frequency, and changes in ambient and winding temperatures. in the conventional design, sophisticated and precise winding machines are used to achieve the nonlinearity compensation [2]–[4]. Some digital signal processing techniques have been suggested to achieve better sensitivity and to implement the signal conditioning circuits [5], [6], [13]. It is reported in [7]–[9] that the artificial neural network (ANN)-based inverse model can effectively compensate for the nonlinearity effect of the sensors.