论文标题
SLSPY：基于Python的系统级控制器合成框架
SLSpy: Python-Based System-Level Controller Synthesis Framework
论文作者
论文摘要
为大型，复杂和分布式系统的合成控制器是一项具有挑战性的任务。文献中存在许多建议的方法，但是从业者很难应用它们 - 大多数提出的合成方法缺乏现成的软件实现，现有的专有组件太严格而无法扩展到通用系统。为了解决这一差距，我们开发了SLSPY，这是一个用于控制器合成，比较和测试的框架。 Slspy实现了一个高度可扩展的软件框架，该框架提供了两个必不可少的工作流程：综合和仿真。这些工作流是由五个概念组件构建的，这些概念组件可以自定义以实现各种综合算法和干扰测试。 SLSPY预先配备了系统级合成（SLS）的工作流程，该工作流程使用户可以轻松自由地指定所需的设计目标和约束。我们使用文献中描述但没有现成的实现的两个示例来证明SLSPY的有效性。我们开源SLSPY促进未来的控制器综合研究和实际用法。
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.
