Predictive control is a flexible control methodology that can optimize performance while satisfying current and voltage constraints. Its application in the power electronics domain is however hampered by the high computational demands associated with it. In this paper, piecewise-affine neural networks are explored to greatly simplify these controllers and allow for an inexpensive implementation in commercial hardware. More specifically, we tackle the problem of enhancing the start-up transient response of a step-down dc-dc converter while also satisfying inductor current constraints. We analyze the neural network architecture, and detail its training and validation procedures. The learned controller is then embedded on an inexpensive 80-MHz microcontroller, and experimental results are provided showing that the whole control algorithm can be executed in under 30 microseconds.

中文翻译：

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分段仿射神经网络对DC-DC电源转换器的嵌入式PWM预测控制

预测控制是一种灵活的控制方法，可以在满足电流和电压约束的同时优化性能。然而，其在电力电子领域的应用受到与之相关的高计算需求的阻碍。在本文中，探索了仿射仿射神经网络，以极大地简化这些控制器，并允许在商业硬件中实现廉价的实现。更具体地说，我们解决了在满足电感器电流限制的同时增强降压型dc-dc转换器的启动瞬态响应的问题。我们分析了神经网络架构，并详细介绍了其训练和验证过程。然后，将学习到的控制器嵌入廉价的80 MHz微控制器中，