CPS: Medium: Safety-Critical Cyber-Physical Systems: From Validation & Verification to Test & Evaluation

该项目的目标是通过集成计算机科学和控制理论的工具来促进自主网络物理系统(CPS)的最新发展。随着自主CP部署的增加--从汽车到航空航天再到机器人系统--迫切需要验证和确认这些系统的特性，从而确保它们的安全运行。这项工作将有助于建立适用于CPS的测试和评估方法的科学基础，特别是当它们以高度动态的方式与其他代理人和世界相互作用时。这有可能为复杂的CP在各种应用领域的开发和部署提供信息：从(半)自动驾驶汽车到航空安全功能，再到工业应用和空间探索的机器人系统。动态CPS的吸引力将被用来扩大对计算和工程的参与。该项目的愿景是为验证和确认在复杂环境中运行的高度动态的安全关键CPS奠定科学基础。关键的新颖性是一种严格的方法，它利用底层非线性动力学上的控制屏障函数来提供集合不变性的保证：用于指定和验证所需特性的安全关键抽象，针对这些特性认证系统级设计的正式方法，以及允许自适应和机器学习与控制屏障函数集成从而保护系统安全和性能规范的设计规则。概念验证实验演示将在自主的、动态的和安全关键的CP上进行，例如机器人系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to advance the state of the art in autonomous Cyber-Physical Systems (CPS) by integrating tools from computer science and control theory. With the rise in deployment of autonomous CPS--from automotive to aerospace to robotic systems--there is a pressing need to verify and validate properties of these systems and thereby ensure their safe operation. The work will help establish the scientific basis for test and evaluation methods applicable to CPS, especially as they interact with other agents and the world in highly dynamic ways. This has the potential to inform the development and deployment of complex CPS in a variety of application domains: from (semi-) autonomous cars, to safety features in aviation, to robotic systems for industrial applications and space exploration. The appeal of dynamic CPS will be utilized to broaden participation in computing and engineering.The vision of this project is to establish the scientific foundations for the verification and validation of highly dynamic safety-critical CPS operating in complex environments. The key novelty is a rigorous approach that leverages control barrier functions on the underlying nonlinear dynamics to provide guarantees of set invariance yielding: safety-critical abstractions on which to specify and verify desired properties, formal methods certifying system-level designs against those properties, and design rules that allow adaptation and machine learning to be integrated with control barrier functions thereby preserving system safety and performance specifications. Proof-of-concept experimental demonstrations will be performed on CPS that are autonomous, dynamic and safety-critical, e.g., robotic systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.