论文标题
通过深层展开，静态输出反馈合成时间延迟线性系统
Static Output Feedback Synthesis of Time-Delay Linear Systems via Deep Unfolding
论文作者
论文摘要
我们提出了一种基于时间延迟线性系统稳定的深层发展方法。深度展开是设计和改进迭代算法的新兴框架，并在信号处理中引起了极大的关注。在本文中，我们提出了一种算法来设计静态输出反馈增益，以通过深层展开稳定时间延迟线性系统。在该算法中，学习部分是由在机器学习社区中开发的神经模型驱动的，而增益验证是通过系统和控制理论中产生的线性矩阵不平等进行的。用数值模拟说明了所提出的算法的有效性。
We propose a deep unfolding-based approach for stabilization of time-delay linear systems. Deep unfolding is an emerging framework for design and improvement of iterative algorithms and attracting significant attentions in signal processing. In this paper, we propose an algorithm to design a static output feedback gain for stabilizing time-delay linear systems via deep unfolding. Within the algorithm, the learning part is driven by NeuralODE developed in the community of machine learning, while the gain verification is performed with linear matrix inequalities developed in the systems and control theory. The effectiveness of the proposed algorithm is illustrated with numerical simulations.
