Multi-objective genetic algorithm optimization of linear proportional solenoid actuator

Abstract

AbstractLinear proportional solenoid (LPS) is widely applied in different linear motion control systems as the electromagnetic actuator since its high reliability and low cost. LPS is difficult to optimize by changing a single variable due to amounts of structural design parameters, and each design parameter has a nonlinear relationship with the static electromagnetic force. This paper aims to improve LPS’s push force and response performance through magnetostatic finite element analysis (FEA) by ANSYS MAXWELL. This study compares FEA 2D model, 3D model and measurement results underrated coil current to verify the accuracy of FEA 2D model. In order to reveal the nonlinear relationship between shape design parameters and electromagnet design objectives, this study compares the influence degree of each variable on each design objective by conventional type LPS 2D FEA model. And for the purpose of improving LPS’s push force and response performance, a multi-objective optimization method has been proposed in this study based on genetic algorithm (GA) and magnetostatic FEA 2D model for optimizing the shape design parameters. All the study results were validated in both static conditions and dynamic conditions. The comparison between manufactured optimal type and conventional type results shows that the static push force in working stroke is improved 30.1%, displacement step response rise time is reduced 5.2% and 43.4%, and force step response rise time is reduced 20.5% and 44.6% with different return spring stiffness. Above all, LPS static and dynamic performance has been improved directly and the validation of proposed optimization method is verified in this paper.