Design optimization of tubular linear voice coil motors using swarm intelligence algorithms

Abstract

This article presents design optimization based on swarm intelligence algorithms of a tubular linear voice coil motor (TLVCM). A magnetic equivalent circuit model is used, allowing a faster and more accurate evaluation of the initial design of the TLVCM. The design requirements are determined, and an initial design is formed based on the design requirements. The TLVCM design is considered a constrained optimization problem with complex linear and nonlinear constraints. The optimization process based on swarm intelligence algorithms is performed to find the optimal solution and improve the performance of the TLVCM. Finally, finite element analysis is used again to verify the optimized results, and different design outputs are compared. According to numerical experimental results, the average thrust is increased by 8.3% and the thrust ripple is reduced by 35.6%. Thus, a highly effective motor design meeting efficiency and performance requirements is achieved.