LAUSR

The original fuzzy-based Taguchi method is restricted with the number of objective functions. Aiming to solve this problem, this paper describes an improved fuzzy-based Taguchi method (IFBTM) coupled with finite-element analysis (FEA) for the multi-objective optimization of direct-drive permanent magnet synchronous motors (DDPMSMs). To reduce the number of dimensions in the fuzzy rule base, the IFBTM uses a factor stratification method to establish multilayer fuzzy rule base. The fuzzy inference system with multilayer fuzzy rule base is introduced to translate the multiple optimization objectives to a single objective. After optimal selection analysis, the best combination of motor structure factors is obtained with small FEA computation. Finally, the performances of DDPMSM are analyzed and compared with those of IFBTM. The results certify the superiority and validity of IFBTM.