Training Neural Networks (NNs) to behave as Model Predictive Control (MPC) algorithms is an effective way to implement them in constrained embedded devices. By collecting large amounts of input-output data, where inputs represent system states and outputs are MPC-generated control actions, NNs can be trained to replicate MPC behavior at a fraction of the computational cost. However, although the composition of the training data critically influences the final NN accuracy, methods for systematically optimizing it remain underexplored. In this paper, we introduce the concept of Optimally-Sampled Datasets (OSDs) as ideal training sets and present an efficient algorithm for generating them. An OSD is a parametrized subset of all the available data that (i) preserves existing MPC information up to a certain numerical resolution, (ii) avoids duplicate or near-duplicate states, and (iii) becomes saturated or complete. We demonstrate the effectiveness of OSDs by training NNs to replicate the University of Virginia's MPC algorithm for automated insulin delivery in Type-1 Diabetes, achieving a four-fold improvement in final accuracy. Notably, two OSD-trained NNs received regulatory clearance for clinical testing as the first NN-based control algorithm for direct human insulin dosing. This methodology opens new pathways for implementing advanced optimizations on resource-constrained embedded platforms, potentially revolutionizing how complex algorithms are deployed.

本研究解决了训练神经网络以实现模型预测控制（MPC）算法的训练数据集优化问题。我们提出了优化抽样数据集（OSDs）的概念和高效生成算法，显著提高了神经网络的准确性，最终在1型糖尿病的自动胰岛素输送中实现了四倍的精确度提升，其中两种训练过的神经网络已获得临床测试的监管许可。这一方法为在资源受限的嵌入式平台上实施复杂算法的优化开辟了新路径。

用于片上模型预测控制的神经网络：构建优化训练数据集的方法及其在1型糖尿病中的应用