Multiobjective hyperparameter optimization is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives, such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.

中文翻译：

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用于同步电机最优前馈扭矩控制的人工神经网络多目标超参数优化

应用多目标超参数优化来寻找用于同步电机最佳前馈扭矩控制 (OFTC) 的最佳人工神经网络 (ANN) 架构。所提出的框架允许针对多个（部分）矛盾的目标（例如所考虑的 ANN 的近似精度和计算负担）系统地识别帕累托最优 ANN。所获得的帕累托最优 ANN 在实时系统上进行训练和实现，并针对非线性磁阻同步机与非帕累托最优 ANN 设计和最先进的 OFTC 方法进行实验测试。最后，根据 ANN 近似理论的最新结果，提供了基于帕累托最优 ANN 的 OFTC 设计和实施指南。