An accurate modeling and design method of inductive displacement sensor

This paper proposes a novel mathematical model that has high accuracy based on a multi-objective optimization algorithm for the inductive displacement sensors, which is popular in industrial production due to their simple design and reliable performance. The proposed model uses composite functions to establish the structural parameters of the coil winding. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve a series of non-dominated problems related to the coil structure parameters with the goal of achieving superior sensor performance. Density clustering sorting is used to select the desired non-dominated solutions. The designed sensor has a nonlinearity of 0.16%, and numerical simulations and physical experiments confirm the effectiveness of the new design method.

Automated summary

This paper proposes a novel mathematical model for inductive displacement sensors based on a multi-objective optimization algorithm, which has high accuracy. The model uses composite functions to establish the coil winding's structural parameters. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to solve non-dominated problems related to the coil structure parameters, aiming for superior sensor performance. Density clustering sorting is utilized to select the desired non-dominated solutions. The designed sensor has a nonlinearity of 0.16%, and numerical simulations and physical experiments confirm the effectiveness of the new design method.