A Fast Method for Statistical Robust Optimization

In electromagnetic design, to counteract the effect of mechanical tolerances and/or assembly inaccuracies on the performance of “optimized” devices, robust design techniques must be adopted. One of the most effective approaches takes advantage from statistical analysis to improve average performance rather than nominal one during the optimization process. Anyway, suitable measures must be taken to reduce the computational burden required by these strategies. In this paper a novel method for statistical design of electromagnetic power devices is presented, combining the strengths of existing methods while offering superior efficiency. It is inspired by Taguchi’s approach and makes use of the unscented transformation. The computational requirements are considerably reduced compared to conventional statistical optimization methods. The proposed approach is implemented for the robust design of a magnetic resonance imaging superconducting device