从反演中学习高阶DC-DC变换器控制
Learning Higher Order DC-DC Converter Control from Inversion
- 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总结：
针对高阶非线性DC-DC变换器控制难题，提出基于模型反演生成监督数据训练神经网络控制器的方法，在Cuk、SEPIC和zeta变换器上验证了有效调节性能。
AI中文摘要：
具有非线性动力学的高阶变换器的控制设计通常很困难。非线性控制引入了巨大的模型复杂性，而线性控制则存在抗扰性能差的问题。本文的目标是开发一种简单的基于神经网络的控制器，该控制器从基于模型的有界反演框架生成的监督轨迹中学习控制律。首先，我们建立了变换器模型的强可逆性。接下来，我们通过定义目标动力学的轨迹模型来规定期望的闭环行为。然后，我们数值上对变换器动力学进行反演，以获得相应的占空比和状态轨迹。我们使用这些基于模型的轨迹作为监督，并训练一个神经网络来逼近所得的状态到占空比控制律。结果表明，学习到的控制器准确地恢复了基于反演的控制动作，并在负载和输入电压扰动下实现了有效的调节。我们在四阶Cuk、SEPIC和zeta变换器上验证了该方法。
英文摘要：
Control design for high-order converters with nonlinear dynamics is generally difficult. Nonlinear controls introduce formidable model complexity whereas linear controls suffer from poor disturbance rejection. Our objective in this paper is to develop a simple neural-network-based controller that learns the control law from supervised trajectories generated by a model-based bounded inversion framework. First, we establish strong invertibility for the converter models. Next, we prescribe the desired closed-loop behavior through a trajectory model that defines the target dynamics. We then invert the converter dynamics numerically to obtain the corresponding duty-cycle and state trajectories. We use these model-based trajectories as supervision and train a neural network to approximate the resulting state-to-duty control law. The results show that the learned controllers accurately recover the inversion-based control action and achieve effective regulation under load and input-voltage disturbances. We validate the method on fourth order Cuk, SEPIC, and zeta converters.
