CAREER: Nonsmooth Control Systems for Societal Networks with Data-Assisted Feedback Loops: Theory and Algorithms

职业：具有数据辅助反馈环的社会网络的非平滑控制系统：理论和算法

• 批准号: 2144076
• 负责人: Jorge Poveda
• 金额: $ 50万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144076&HistoricalAwards=false
• 关键词: CAREER; Nonsmooth; Control; Systems; Societal

The overarching goal of this CAREER proposal is to formally advance the analysis and synthesis of hybrid and non-smooth data-assisted controllers, which are algorithms that: (a) incorporate data-driven mechanisms in the closed-loop system for the purpose of real-time estimation, learning, and adaptation; and (b) are characterized by hybrid and non-smooth dynamical systems able to meet stringent robustness, stability and transient demands that are mathematically unachievable using smooth control techniques. The research is motivated by technological advances that have made devices for actuation, sensing, computation, and communication increasingly portable, inexpensive, and prevalent in networked engineering systems, including robotic networks, the power grid, and connected transportation systems. In these applications, the increasing complexity of the underlying (hybrid) dynamical systems and their corresponding decision-making problems have exposed the fundamental limitations of traditional smooth feedback control and optimization methods. The research plan will seek to overcome these limitations by developing a new paradigm of data-assisted network control based on hybrid control theory for multi-agent systems deployed over cyber-physical infrastructure. The project incorporates a strong educational and outreach plan that will involve active recruitment and mentorship of students from diverse backgrounds via summer enrichment camps, as well as after-school programs for middle and high-school students. The outreach plan also includes the development of a regional workshop in the broad areas of control and autonomous systems, as well as active collaborations with industry and national laboratories to inform and guide the research. The research project will leverage recent mathematical tools developed in hybrid control theory, further integrated and developed in three cohesive research thrusts: 1) The development of robust data-assisted switched and non-smooth controllers able to overcome obstructions to smooth stabilization, tracking, and optimization by switching between multiple feedback-based algorithms that use concurrently real-time and recorded data generated by the system under control; 2) The robust coordination of multi-agent data-assisted controllers to synergistically exploit their individual capabilities to obtain a desired network-wide performance, while preserving suitable scalability properties with respect to the size of the network and their data requirements; 3) The analysis and synthesis of strategic data-assisted controllers for multi-agent systems where certain individual agents systematically and dynamically manipulate their data for the purpose of deception without inducing unstable behaviors in the closed-loop system. The theoretical principles uncovered in the project, as well as the proposed algorithms, will be tested and validated in realistic numerical and experimental engineering 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.

该CAREER提案的总体目标是正式推进混合和非平滑数据辅助控制器的分析和合成，这些控制器是以下算法：（a）将数据驱动机制纳入闭环系统中，以实现实时估计，学习和适应;和（B）的特征在于能够满足严格鲁棒性的混合和非光滑动态系统，使用平滑控制技术在数学上无法实现的稳定性和瞬态需求。这项研究的动机是技术进步，使驱动，传感，计算和通信设备越来越便携，廉价，并在网络工程系统中普遍，包括机器人网络，电网和连接的运输系统。在这些应用中，底层（混合）动态系统及其相应的决策问题的复杂性不断增加，暴露了传统的光滑反馈控制和优化方法的根本局限性。研究计划将寻求克服这些限制，开发一个新的数据辅助网络控制模式的基础上混合控制理论的多代理系统部署在网络物理基础设施。该项目包括一个强有力的教育和推广计划，将涉及通过夏令营积极招募和指导来自不同背景的学生，以及为初中和高中学生提供课后计划。外联计划还包括在控制和自主系统等广泛领域举办一次区域讲习班，以及与工业界和国家实验室积极合作，为研究提供信息和指导。该研究项目将利用混合控制理论中开发的最新数学工具，进一步整合和开发三个有凝聚力的研究重点：1）开发鲁棒数据辅助切换和非平滑控制器，能够通过在多个基于反馈的算法之间切换来克服平滑稳定，跟踪和优化的障碍，这些算法同时使用由受控系统生成的实时和记录数据; 2）多代理数据辅助控制器的鲁棒协调，以协同地利用它们各自的能力来获得期望的网络范围的性能，同时保留关于网络的大小和它们的数据要求的合适的可扩展性属性; 3）多目标系统的策略数据辅助控制器的分析与综合代理系统，其中某些个体代理系统地和动态地操纵它们的数据以达到欺骗的目的，而不会在闭环系统中引起不稳定的行为。该项目中发现的理论原理以及提出的算法将在现实的数值和实验工程系统中进行测试和验证。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。

项目成果

Online Optimization of Switched LTI Systems Using Continuous-Time and Hybrid Accelerated Gradient Flows

• DOI: 10.1016/j.automatica.2022.110579
• 发表时间: 2020-08
• 期刊: Autom.
• 作者: G. Bianchin;J. Poveda;E. Dall’Anese

Data-Assisted Vision-Based Hybrid Control for Robust Stabilization with Obstacle Avoidance via Learning of Perception Maps

基于数据辅助视觉的混合控制，通过感知图学习实现鲁棒稳定和避障

• 发表时间: 2022
• 期刊: Proceedings of American Control Conference (2022
• 作者: A. Murillo;J. I. Poveda
• 通讯作者: J. I. Poveda