SLSpy：基于Python的系统级控制器综合框架
SLSpy: Python-Based System-Level Controller Synthesis Framework
摘要：
为大型、复杂和分布式系统综合控制器是一项具有挑战性的任务。在文献中存在许多建议的方法，但从业者很难应用它们-大多数建议的合成方法缺乏现成的软件实现，现有的专有组件过于僵化，无法扩展到一般系统。为了解决这一差距，我们开发SLSpy，控制器合成，比较和测试的框架。SLSpy实现了一个高度可扩展的软件框架，提供了两个基本的工作流程：合成和模拟。工作流程由五个概念组件组成，可以定制以实现各种合成算法和干扰测试。SLSpy预先配备了系统级合成（SLS）的工作流程，使用户能够轻松自由地指定所需的设计目标和约束。我们证明了SLSpy的有效性，使用两个例子，已在文献中描述，但没有准备使用的实现。我们开源SLSpy，以方便未来的控制器综合研究和实际使用。
英文摘要：
Synthesizing controllers for large, complex, and distributed systems is a challenging task. Numerous proposed methods exist in the literature, but it is difficult for practitioners to apply them -- most proposed synthesis methods lack ready-to-use software implementations, and existing proprietary components are too rigid to extend to general systems. To address this gap, we develop SLSpy, a framework for controller synthesis, comparison, and testing. SLSpy implements a highly extensible software framework which provides two essential workflows: synthesis and simulation. The workflows are built from five conceptual components that can be customized to implement a wide variety of synthesis algorithms and disturbance tests. SLSpy comes pre-equipped with a workflow for System Level Synthesis (SLS), which enables users to easily and freely specify desired design objectives and constraints. We demonstrate the effectiveness of SLSpy using two examples that have been described in the literature but do not have ready-to-use implementations. We open-source SLSpy to facilitate future controller synthesis research and practical usage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
