论文标题
带有输入饱和的线性系统的深度基于展开的输出反馈控制设计
Deep unfolding-based output feedback control design for linear systems with input saturation
论文作者
论文摘要
在本文中，我们为输入饱和度对系统的输出反馈控制提出了一个基于深度展开的框架。尽管饱和度通常在几个实际的控制系统中产生，但仍然存在稀缺的有效设计方法，可以直接处理饱和操作员的严重非线性。在本文中，我们旨在设计一个反垃圾控制器，以通过从闭环系统的数值模拟中学习闭环系统的稳定区域。我们在本文中提出的数据驱动框架是基于一种称为神经普通微分方程的深度学习技术。在我们的框架内，我们首先使用深入学习技术获得了候选控制器，然后通过文献中已经建立的现有理论结果对此进行测试，从而避免了常规设计方法中的计算挑战以及理论上保证系统的性能。我们的数值模拟表明，所提出的框架可以显着胜过基于线性矩阵不平等的常规设计方法。
In this paper, we propose a deep unfolding-based framework for the output feedback control of systems with input saturation. Although saturation commonly arises in several practical control systems, there is still a scarce of effective design methodologies that can directly deal with the severe non-linearity of the saturation operator. In this paper, we aim to design an anti-windup controller for enlarging the region of stability of the closed-loop system by learning from the numerical simulations of the closed-loop system. The data-driven framework we propose in this paper is based on a deep-learning technique called Neural Ordinary Differential Equations. Within our framework, we first obtain a candidate controller by using the deep-learning technique, which is then tested by the existing theoretical results already established in the literature, thereby avoiding the computational challenge in the conventional design methodologies as well as theoretically guaranteeing the performance of the system. Our numerical simulation shows that the proposed framework can significantly outperform a conventional design methodology based on linear matrix inequalities.
