发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合微分平坦性与神经网络策略学习的框架，用于控制DC-DC变换器，在降压、升压等拓扑上验证了轨迹一致性与扰动下的稳定调节。
AI 中文摘要
在这项工作中，我们提出了一种简单的方法来设计一个小型神经网络以控制功率变换器。基于学习的方法已成为在宽工作点变化下控制功率变换器的一种有前景的方法。然而，这些方法中的许多并未利用变换器动力学的解析结构，因此搜索了不必要广泛的策略类别。我们通过一个结合基于模型的轨迹生成与策略学习的系统框架来解决这一差距。首先，我们利用平均变换器模型的微分平坦性来生成可行的状态和受约束的占空比轨迹，其中占空比作为控制努力信号。然后，对于策略学习，我们使用这些模型生成的轨迹来构建标记的状态到占空比数据，并训练一个多层感知器以静态映射来近似控制律。我们将此框架应用于降压、升压和降压-升压变换器，通过依赖于拓扑的平坦输出选择和共同的学习流程。仿真显示，学习到的占空比轨迹与模型生成的轨迹之间高度一致，并且在大的同步输入和负载扰动下具有稳定的调节性能。
英文摘要
In this work, we present a simple method to engineer a small neural network to control power converters. Learning-based methods have emerged as a promising approach for power converter control over wide operating point variations. However, many of these approaches do not exploit the analytical structure of converter dynamics and, therefore, search over unnecessarily broad policy classes. We address this gap with a systematic framework that combines model-based trajectory generation with policy learning. First, we use differential flatness of averaged converter models to generate feasible state and constrained duty cycle trajectories where duty acts as a control effort signal. Then for policy learning, we use these model-generated trajectories to construct labeled state-to-duty data and train a multilayer perceptron to approximate the control law with a static map. We apply this framework across buck, boost, and buck-boost converters through topology-dependent flat output selection and a common learning pipeline. Simulations show close agreement between learned and model-generated duty trajectories and stable regulation under large synchronous input and load disturbances.
CommentsAccepted to 2026 65th IEEE Conference on Decision and Control (CDC)
