Title: Predictive safety filter using system level synthesis Show Chinese title

Title: 使用系统级综合的预测安全滤波器

Abstract: Safety filters provide modular techniques to augment potentially unsafe control inputs (e.g. from learning-based controllers or humans) with safety guarantees in the form of constraint satisfaction. In this paper, we present an improved model predictive safety filter (MPSF) formulation, which incorporates system level synthesis techniques in the design. The resulting SL-MPSF scheme ensures safety for linear systems subject to bounded disturbances in an enlarged safe set. It requires less severe and frequent modifications of potentially unsafe control inputs compared to existing MPSF formulations to certify safety. In addition, we propose an explicit variant of the SL-MPSF formulation, which maintains scalability, and reduces the required online computational effort - the main drawback of the MPSF. The benefits of the proposed system level safety filter formulations compared to state-of-the-art MPSF formulations are demonstrated using a numerical example. Show Chinese abstract

Abstract: 安全滤波器提供了一种模块化技术，可以以约束满足的形式为潜在的不安全控制输入（例如，基于学习的控制器或人类的控制输入）提供安全保障。本文提出了一种改进的模型预测安全滤波器（MPSF）公式，该公式在设计中结合了系统级综合技术。由此产生的SL-MPSF方案确保了线性系统的安全性，在有界干扰的情况下，其安全集被扩大。与现有的MPSF公式相比，它对潜在的不安全控制输入的修改较少且频率较低即可保证安全性。此外，我们提出了SL-MPSF公式的显式变体，该变体保持了可扩展性，并减少了所需的在线计算努力——这是MPSF的主要缺点。通过数值实例展示了所提出的系统级安全滤波器公式与最先进的MPSF公式的优点。