CPS: DFG Joint: Medium: Collaborative Research: Data-Driven Secure Holonic control and Optimization for the Networked CPS (aDaptioN)

关键网络物理网络基础设施（CPNI）的分散/分布式控制和优化将提高配电网的鲁棒性，安全性和弹性，直接影响公民的生活和国民经济。所提出的控制和优化架构是灵活的，适应不断变化的操作场景，快速准确地响应，提供更好的可扩展性和鲁棒性，并通过利用大量传感器数据，分布式计算和边缘计算，即使在推向边缘时也能安全地操作系统。该算法和平台将以开源和免版税的方式发布，项目团队将与行业成员和研究人员合作，为其他CPNI更广泛地使用所开发的算法。作为拟议工作的一部分，开发的工件将被整合到现有的本科生和研究生相关课程中。本科生将通过补充和代表性不足的研究和工程预科学生将通过现有的外展活动在国内机构，包括想象U计划和4-H青少年夏令营计划和太平洋西北部路易斯斯托克斯少数民族联盟参与。 此外，项目团队计划在第三年组织一次研讨会，以展示拟议的控制和优化架构的基本概念和应用，以推进CPNI。所开发的解决方案可以扩展到多个CPNI中的应用范围，超出了所提出的工作中讨论的用例。由于可变的网络和计算延迟、消息到达的若干交错、组件的不同故障模式，和网络安全威胁导致了几个基本的理论问题。提出的工作提供了许多新的解决方案，包括（a）自适应和延迟感知控制算法，（B）预测控制和分布式优化与现实的网络物理约束，（c）威胁共享，数据驱动的检测和缓解网络安全，（d）计算节点的协调和管理，（e）知识学习和共享。拟议的解决方案将是朝着推进CPNI和下一代CPNI工程的基本原则迈出的一步。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The proposed decentralized/distributed control and optimization for the critical cyber-physical networked infrastructures (CPNI) will improve the robustness, security and resiliency of the electric distribution grid, which directly impacts the life of citizens and national economy. The proposed control and optimization architectures are flexible, adapt to changing operating scenarios, respond quickly and accurately, provide better scalability and robustness, and safely operate the system even when pushed towards the edges by leveraging massive sensor data, distributed computation, and edge computing. The algorithms and platform will be released open source and royalty-free and the project team will work with industry members and researchers for wider usage of the developed algorithms for other CPNI. Developed artifacts as part of the proposed work will be integrated in existing undergraduate and graduate related courses. Undergraduate students will be engaged in research through supplements and underrepresented and pre-engineering students will be engaged through existing outreach activities at home institutions including Imagine U program and 4-H Teens summer camp programs and the Pacific Northwest Louis Stokes Alliance for Minority Participations. Additionally, project team plans to organize a workshop in the third year to demonstrate the fundamental concepts and applications of the proposed control and optimization architecture to advance CPNI. Developed solutions can be extended for range of applications in multiple CPNIs beyond use cases discussed in the proposed work.While the proposed control architecture with edge computing offer great potential; coordinating decentralized control and optimization is extremely challenging due to variable network and computational delays, several interleavings of message arrivals, disparate failure modes of components, and cyber security threats leading to several fundamental theoretical problems. Proposed work offers number of novel solutions including (a) adaptive and delay-aware control algorithms, (b) Predictive control and distributed optimization with realistic cyber-physical constraints, (c) threat sharing, data-driven detection and mitigation for cyber security, (d) coordination and management of computing nodes, (e) knowledge learning and sharing. Proposed solutions will be a step towards advancing fundamentals in CPNI and in engineering next generation CPNI. The proposed work also aims to use high fidelity testbed to evaluate developed algorithms and tools for specific CPNI: electric distribution grid.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.