Collaborative Research: Closed-loop Optimization and Control of Physical Networks Subject to Dynamic Costs, Constraints, and Disturbances

协作研究：受动态成本、约束和干扰影响的物理网络的闭环优化和控制

基本信息

批准号：
2044946
负责人：
Emiliano Dall'Anese
金额：
$ 30万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044946&HistoricalAwards=false
关键词：
Collaborative Research Closed loop Optimization

项目摘要

This project will advance a fundamentally new control framework, utilizing streams of heterogeneous data to optimize the behavior of complex and dynamic networked systems with pervasive sensing and computing capabilities, operating in uncertain and changing environments. Existing workhorse control and optimization methodologies assume a large separation of time scales, sufficient to justify complete decoupling of the optimization and control tasks. However, this assumption is increasingly invalid for modern critical infrastructure and social platforms. This project represents a new approach for optimal and reliable decision-making on time scales comparable to the dynamics of the underlying physical and logistic systems, by using new mathematical principles of analysis and synthesis to control the collective behavior of agents and the underlying physical dynamics. The key concept is to continuously drive the dynamical system towards solution trajectories of optimization problems that have costs, constraints, and inputs which change over time. In the context of future transportation networks, the approach is well-aligned with the objective of moving people and cargo efficiently and sustainably, and with the integration of connected and autonomous vehicles. Similar application opportunities occur in areas such as energy, robotics, and autonomous systems, with the common feature of interconnected cooperative and non-cooperative agents interacting via multiple heterogeneous physical and virtual networks. The project will also impact undergraduate and graduate engineering students, and K-12 students through a comprehensive outreach and educational plan that includes STEM camps, engaging activities to promote the recruitment of female students and students from under-served communities and minority schools into the STEM pipeline, and curriculum enhancement initiatives.Traditional decision-making architectures in networked systems and critical infrastructures are grounded on explicit spatio-temporal boundaries between model-based network-level optimization (producing setpoints in a feed-forward fashion) and local closed-loop control (regulating the dynamical system to the setpoints while rejecting disturbances). The modus operandi of these traditional architectures has worked well in settings where the underlying dynamics of the physical systems are slower than the solution time required by network-level optimization tasks, network models and data structures are available, and problem inputs can be pervasively collected in a timely and reliable manner. Such assumptions, however, are becoming increasingly inadequate in dynamic settings where batch approaches fail to solve the underlying optimization problems on a time scale that matches the dynamics of the networked physical systems, physical models (embedded into the optimization task) are difficult to estimate accurately, and (unknown) disturbances evolve rapidly and unpredictably. This project will generate new mathematical principles for the synthesis and analysis of online data-based algorithms that drive the collective behavior of agents and physical dynamics to desired operational points. In particular, the desired equilibrium points coincide with solution trajectories of time-varying optimization problems formalizing performance metrics and operational constraints associated with the dynamical system. The interconnected-system framework under study compresses the time scales between control and optimization tasks to continuously drive the dynamic behavior of physical systems to network-optimal and stable points. The research seeks to expand the class of problems to which this project vision can be applied, develop predictive controllers with information streams, and synthesize novel distributed algorithmic solutions for interconnected systems. The technical approach focuses on networked transportation systems as the arena to materialize the theoretical and algorithmic advances and provide innovative control and optimization strategies. Beyond transportation, benefits are expected to propagate in the broader optimization and control communities, with applications in multiple domains including control of epidemics, robotic networks, social networks, and energy infrastructures.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.

该项目将利用异质数据流来提高一个新的控制框架，以优化具有普遍感测和计算能力的复杂和动态网络系统的行为，在不确定和不断变化的环境中运行。现有的锻炼控制和优化方法假设时间尺度的分离很大，足以证明对优化和控制任务的完全解耦。但是，对于现代关键基础架构和社会平台而言，这一假设越来越无效。该项目代表了一种新的方法，可以通过使用新的数学原理和合成的新数学原理来控制代理的集体行为以及基本的物理动力学，从而可以按时间量表与基础物理和逻辑系统的动态相媲美。关键概念是将动态系统持续发展到具有成本，约束和随时间变化的优化问题的解决方案轨迹。在未来的运输网络的背景下，该方法是有效，可持续的，以及连接和自动驾驶汽车的整合的目的。类似的应用机会发生在能源，机器人技术和自主系统等领域，具有通过多个异质物理和虚拟网络相互作用的相互联系和非合作代理的共同特征。该项目还将通过一项全面的外展和教育计划来影响本科和研究生工程专业的学生，以及K-12学生，包括STEM营地，促进女学生和服务不足社区和少数群体学校的招募活动的招聘活动，并在课程增强启动中招募了STEM和少数群体的学生。基于模型的网络级优化（以馈送方式生产设定点）和本地闭环控制（在拒绝干扰的同时将动态系统调节到设定点）。这些传统体系结构的作案操作在设置中效果很好，在这些设置中，物理系统的基本动力学比网络级优化任务，网络模型和数据结构所需的解决方案时间慢，并且可以及时且可靠的方式在及时收集问题输入。但是，这种假设在动态设置中变得越来越不足，因为批处理方法无法在时间尺度上解决与网络物理系统的动态相匹配的潜在优化问题，物理模型（嵌入到优化任务中）难以准确估算，并且（不知名）迅速地进化了且无法进行不知情。该项目将生成新的数学原理，用于综合和分析基于数据的算法，这些算法将代理和物理动态的集体行为推向所需的操作点。特别是，所需的平衡点与时变优化问题的解决方案轨迹正式化了性能指标和与动力学系统相关的操作约束。研究下的互连系统框架压缩了控制任务和优化任务之间的时间尺度，以连续驱动物理系统的动态行为，从而使网络最佳和稳定点。该研究旨在扩大可以应用该项目愿景的问题类别，通过信息流开发预测控制器，并合成用于互连系统的新型分布式算法解决方案。技术方法着重于网络运输系统作为实现理论和算法进步的领域，并提供创新的控制和优化策略。除了运输外，预计收益还将在更广泛的优化和控制社区中传播，其中包括控制流行病，机器人网络，社交网络和能源基础架构的多个领域。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力功能和广泛影响的评估来审查CRETIRIA的评估，并被认为是值得通过评估的支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Time-Varying Optimization of LTI Systems Via Projected Primal-Dual Gradient Flows

DOI：
10.1109/tcns.2021.3112762
发表时间：
2021-01
期刊：
IEEE Transactions on Control of Network Systems
影响因子：
4.2
作者：
G. Bianchin;J. Cortés;J. Poveda;E. Dall’Anese
通讯作者：
G. Bianchin;J. Cortés;J. Poveda;E. Dall’Anese

Self-Optimizing Traffic Light Control Using Hybrid Accelerated Extremum Seeking

使用混合加速极值搜索的自优化交通灯控制

DOI：
发表时间：
2021
期刊：
60th IEEE Conference on Decision and Control (CDC
影响因子：
0
作者：
Felipe Galarza-Jimenez;Jorge I. Poveda;Ronny Kutadinata;Lele Zhang;Emiliano Dall’Anese
通讯作者：
Emiliano Dall’Anese

Online optimization of LTI systems under persistent attacks: Stability, tracking, and robustness