Particle Swarm Design Optimization of Transverse Flux Linear Motor for Weight Reduction and Improvement of Thrust Force

Abstract

Particle swarm optimization (PSO) is a computational intelligence-based technique that is not largely affected by the size and nonlinearity of the problem and can converge to the optimal solution in many problems where most analytical methods fail to converge. The PSO algorithm is applied to the design optimization problem of a permanent-magnet type transverse flux linear motor (TFLM). The objective of the optimization is to reduce the motor weight while maximizing the thrust force as well as minimizing the detent force of the motor. The stator pole length, the air gap length, the winding window width, and the stator pole width define the search space for the optimization problem. Response surface methodology (RSM) is well adapted to obtain an analytical model of the motor weight, detent force, and thrust force. The RSM enables objective functions to be easily created and a great computational time to be saved. Finite element computations are used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. The finite element analysis based model is verified by experimental results. The effectiveness of the proposed PSO model is then compared with that of the conventional optimization models and genetic algorithms model. With this proposed PSO technique, the weight of the initially designed TFLM and its detent force can be reduced, as well as its thrust force can be increased.