Multi-objective worst-case scenario robust optimal design of switched reluctance motor incorporated with FEM and Kriging

In this paper, one multi-objective robust optimization algorithm is applied to the optimal design of switched reluctance motor. The performance robustness against uncertainty in design variables is evaluated utilizing the first order sensitivity assisted-worst case scenario approximation. In order to reduce the computing cost required by the finite element analysis, the Kriging surrogate model is used to predict performance of switched reluctance motor during optimization process. With the help of multi-objective particle warm optimization algorithm, a set of robust optimal designs are obtained through making a balance between maximizing average torque and minimizing torque tipple.