One of the main obstacles to applying the robust optimization for the permanent magnet motors is the high computational burden, which is mainly caused by the robustness evaluation considering the manufacturing uncertainties. In this article, efforts are made to speed up the process of robust optimization, from the aspects of reducing the computational cost, and avoiding the computing resources being wasted. Firstly, several widely used methods, aiming to identify the worst-case performance under uncertainties, are examined, and compared. And the efficient worst-uncertain-combination-analysis (WUCA) method is consequently adopted to significantly reduce the computational cost of robustness evaluation. From another aspect, the parameter rounding (PR) operation is inevitable due to the constraint of manufacturing accuracy, but its adverse effects on the optimization are often ignored. It is found that parts of the computing resources might be wasted during the optimization. A new PR during the calculation (PRDC) method is proposed, and incorporated into the process of robust optimization. Compared with the conventional one, similar results can be achieved with the computational time reduced by approximately $\text{11}\%$ in the PRDC modified robust optimization.

中文翻译：

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减少永磁电机稳健优化计算负担的方法

对永磁电机应用鲁棒优化的主要障碍之一是高计算负担，这主要是由考虑制造不确定性的鲁棒性评估引起的。本文从降低计算成本、避免计算资源浪费等方面努力加快鲁棒优化的过程。首先，检查并比较了几种广泛使用的方法，旨在确定不确定性下的最坏情况性能。并且因此采用了有效的最坏不确定性组合分析（WUCA）方法来显着降低稳健性评估的计算成本。另一方面，由于制造精度的限制，参数舍入（PR）操作是不可避免的，但它对优化的不利影响往往被忽视。发现在优化过程中可能会浪费部分计算资源。提出了一种新的计算过程中的PR（PRDC）方法，并将其纳入到鲁棒优化的过程中。与传统的相比，在 PRDC 改进的鲁棒优化中，计算时间减少了大约 $\text{11}\%$ 可以获得类似的结果。