CRII: CPS: High-Performance Adaptive Hybrid Feedback Algorithms for Real-Time Optimization and Learning in Networked Transportation Systems

传感、计算和通信领域的最新技术进步，以及现代数据革命，正在给与社会紧密相连的几个工程系统的操作和控制带来革命性的变化。特别是，智能交通系统是社会集成工程系统的突出例子，在这种系统中，大规模数据集、自动化和反馈控制的结合可以在提高出行效率、安全性和可靠性方面产生巨大的社会影响。然而，为了充分利用这一潜力，需要严格解决算法设计和分析层面的不同基本开放问题。这些问题包括如何设计基于数据驱动的反馈优化和控制算法，这些算法鲁棒、高效，适合部署在高度动态和复杂的系统中；以及如何证明和描述这些算法的基本局限性。受这些问题的启发，该项目将开发新的分析和数据驱动算法工具，用于智能交通系统的实时优化和控制。提出的研究的动机是基于反馈的算法显示出巨大潜力的两个特定应用：动态定价控制，最近在美国、欧洲和亚洲的几个城市实施；还有城市交通灯控制，这是现在人口密集城市的基础设施技术。该项目包括对高中生和研究生的外联和指导，以及通过教育活动积极传播结果。该项目的主要目标是设计和分析新颖的自适应和鲁棒的数据驱动控制和优化算法，并在运输系统中提供可证明的性能保证。与传统方法不同，该算法将结合加速以最大限度地利用，同时使用信息丰富的数据集来放松传统的激进勘探要求。所有这些都不会牺牲结构的坚固性，这是与交通网络动态安全互联的基础。为了实现这一目标，将使用混合动力系统理论的工具开发和分析基于反馈的算法，该算法适用于复杂网络物理系统的研究。所提出算法的自适应特性将使它们能够实时处理交通网络中出现的复杂的离散时间和连续时间动态。为了证明闭环的稳定性和鲁棒性，该项目将利用时间尺度分离技术和被动工具。算法的性能将在现实环境中通过大量的实验和数值模拟来验证。该奖项反映了美国国家科学基金会的法定使命，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。

Recent technological advances in the areas of sensing, computation, and communication, as well as the modern data revolution, are bringing transformative changes to the operation and control of several engineering systems that are tightly connected with society. In particular, intelligent transportation systems stand as prominent examples of socially integrated engineering systems where the combination of large-scale data sets, automation, and feedback control could have a tremendous societal impact in terms of improvement in travel efficiency, safety, and reliability. Nevertheless, to fully exploit this potential, different fundamental open questions at the level of algorithmic design and analysis need to be rigorously addressed. These questions include how to design data-driven feedback-based optimization and control algorithms that are robust, efficient, and suitable for deployment in highly dynamic and complex systems; and how to certify and characterize the fundamental limitations of these algorithms. Motivated by these questions, this project will develop novel analytical and data-driven algorithmic tools for the real-time optimization and control of intelligent transportation systems. The proposed research is motivated by two particular applications where feedback-based algorithms have shown tremendous potential: dynamic pricing control, which has recently been implemented in several cities across the United States, Europe and Asia; and urban traffic light control, which is now a fundamental infrastructure technology in dense urban cities. This project includes outreach and mentoring of high school and graduate students, as well as an active dissemination of the results via educational initiatives.The main objective of this project is the design and analysis of novel adaptive and robust data-driven control and optimization algorithms with provable performance guarantees in transportation systems. Unlike traditional approaches, the algorithms will incorporate acceleration to maximize exploitation, while simultaneously using information-rich data sets to relax traditional aggressive exploration requirements. All of this, without sacrificing structural robustness properties, which are fundamental for a safe interconnection with the dynamics of the transportation network. To achieve this, the feedback-based algorithms will be developed and analyzed using tools from hybrid dynamical system's theory, which is suitable for the study of complex cyber-physical systems. The adaptive nature of the proposed algorithms will allow them to cope in real-time with the complex discrete-time and continuous-time dynamics that emerge in transportation networks. To certify closed-loop stability and robustness properties, the project will exploit time scale separation techniques and passivity tools. The performance of the algorithms will be validated by extensive experimental and numerical simulations in realistic environments.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.