CAREER: Data-Driven Control of Dynamical Networks: Robustness, Risk, and Network Architectures

职业：动态网络的数据驱动控制：鲁棒性、风险和网络架构

• 批准号: 2047040
• 负责人: Tyler Summers
• 金额: $ 50万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047040&HistoricalAwards=false
• 关键词: CAREER; Data; Driven; Control; Dynamical

Recent spectacular technological advances and unprecedented availability of data are providing opportunities to fundamentally reimagine the control and information architectures and algorithms for emerging complex networked systems, such as autonomous robot teams for transportand delivery and energy grids with massive renewable penetration. However, these networks are posing major challenges for safe, efficient, and robust operation and control. Their accelerating complexity, especially from rapid integration of machine learning components, threatens to outpace our understanding of their robustness properties and lead to severe failure and safety risks. This project will build a rigorous framework for analysis and design of robust, risk-constrained data-driven control algorithms and architectures for dynamical networks. The proposed innovations will have broad societal impact by enabling enhancements to the safety, efficiency and robustness of various emerging complex networks that are vital to future society. The application focus will be on autonomous energy grids and distributed multi-robot teams, but the fundamental knowledge advancements can also impact other emerging critical infrastructure networks. Furthermore, the integrated research and education plan features an extensive array of activities that will train scientists and engineers and promote public understanding of data-driven control in dynamical networks, especially around issues of robustness, risk, and safety.This project will significantly advance knowledge via a transformative robust and risk-aware integration of model-based control and data-based learning approaches that explicitly incorporate uncertainty from finite-data estimates. The overall approach will combine performance and robust-ness guarantees from model-based stochastic control, dynamic game theory, and risk-based distributionally robust optimization with modern non-asymptotic martingale concentration bounds and bootstrap techniques from statistics. Robustness to uncertainties in finite-data model estimates will be explicitly incorporated in two innovative ways: (1) a rich stochastic dynamic game framework that combines adversarial inputs and multiplicative noise to promote robustness to both highly-structured parametric and non-parametric uncertainties; (2) the use of axiomatic risk theory and distributionally robust optimization to guarantee meaningful risk-based safety constraints. On this basis, the project will develop techniques for fully data-driven dynamic output feedback control, active exploration, and locally optimal, robust, and provable convergent algorithms for nonlinear systems using iterative stochastic dynamic games with locally learned and refined models. The proposed research will also advance knowledge by developing innovative self-tuning architectures and regularized policy optimization algorithms for network control architecture design, and illuminating how underlying network structure and size impose fundamental limits on data-driven control and estimation. The approach will be illustrated, refined, and validated via numerical simulations and hardware testbed experiments for autonomous energy grids and distributed heterogeneous multi-robot teams. A complementary education plan will: (1) create interactive exhibitions via extensive educational outreach programs at the Perot Museum of Nature & Science and UT Dallas; (2) enhance curriculum for dynamical networks with new courses, interactive web technologies, and project-based learning; (3) mentor senior capstone design projects and undergraduate researchers that directly support the proposed research.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.

最近惊人的技术进步和前所未有的数据可用性为从根本上重新构想新兴复杂网络系统的控制和信息架构和算法提供了机会，例如用于运输和交付的自主机器人团队以及具有大规模可再生能源渗透的能源电网。然而，这些网络对安全、高效和稳健的操作和控制提出了重大挑战。它们不断加速的复杂性，特别是机器学习组件的快速集成，可能会超过我们对其鲁棒性的理解，并导致严重的故障和安全风险。该项目将建立一个严格的框架，用于分析和设计动态网络的鲁棒，风险约束的数据驱动控制算法和架构。拟议的创新将通过增强对未来社会至关重要的各种新兴复杂网络的安全性，效率和鲁棒性来产生广泛的社会影响。应用的重点将是自主能源网和分布式多机器人团队，但基础知识的进步也可能影响其他新兴的关键基础设施网络。此外，综合研究和教育计划的特点是广泛的活动，将培训科学家和工程师，并促进公众对动态网络中数据驱动控制的理解，特别是围绕鲁棒性，风险，该项目将通过基于模型的控制和数据的变革性的强大和风险意识的集成，基于学习的方法，明确纳入有限数据估计的不确定性。整体方法将结合联合收割机性能和鲁棒性保证基于模型的随机控制，动态博弈论，基于风险的分布鲁棒优化与现代非渐近鞅浓度界和自举技术统计。有限数据模型估计中对不确定性的鲁棒性将以两种创新方式明确纳入：（1）丰富的随机动态博弈框架，结合对抗性输入和乘性噪声，以提高对高度结构化参数和非参数不确定性的鲁棒性;（2）使用公理风险理论和分布鲁棒优化，以保证有意义的基于风险的安全约束。在此基础上，该项目将开发完全数据驱动的动态输出反馈控制技术，积极探索，以及使用具有局部学习和细化模型的迭代随机动态游戏的非线性系统的局部最优，鲁棒和可证明收敛算法。拟议的研究还将通过开发用于网络控制架构设计的创新自调优架构和正则化策略优化算法来推进知识，并阐明底层网络结构和大小如何对数据驱动的控制和估计施加根本限制。该方法将说明，完善，并通过数值模拟和硬件测试台实验的自主能源电网和分布式异构多机器人团队验证。一个补充教育计划将：（1）通过在佩罗自然科学博物馆和UT达拉斯广泛的教育推广计划创建互动展览&;（2）通过新课程、交互式网络技术和基于项目的学习来加强动态网络的课程;（3）指导直接支持拟议研究的高级顶点设计项目和本科生研究人员。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Policy Iteration for Multiplicative Noise Output Feedback Control

乘性噪声输出反馈控制的策略迭代

• DOI: 10.1109/cdc51059.2022.9993098
• 发表时间: 2022
• 期刊: IEEE Conference on Decision and Control
• 作者: Gravell, Benjamin;Gargiani, Matilde;Lygeros, John;Summers, Tyler H.
• 通讯作者: Summers, Tyler H.

PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation

• 发表时间: 2022-02
• 期刊: ArXiv
• 作者: Matilde Gargiani;Andrea Zanelli;Andrea Martinelli;T. Summers;J. Lygeros

Risk-Bounded Temporal Logic Control of Continuous-Time Stochastic Systems

• DOI: 10.23919/acc53348.2022.9867734
• 发表时间: 2022-04
• 期刊: 2022 American Control Conference (ACC)
• 作者: Sleiman Safaoui;Lars Lindemann;I. Shames;T. Summers

Risk-Averse RRT* Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Systems Under Uncertainty

针对不确定性下的非线性机器人系统使用非线性转向和跟踪控制器进行风险规避 RRT* 规划

• DOI: 10.1109/iros51168.2021.9636834
• 发表时间: 2021
• 期刊: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Safaoui, Sleiman;Gravell, Benjamin J.;Renganathan, Venkatraman;Summers, Tyler H.
• 通讯作者: Summers, Tyler H.

Self-Tuning Network Control Architectures

• DOI: 10.1109/cdc51059.2022.9992780
• 发表时间: 2022-12
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC)
• 作者: T. Summers;Karthik Ganapathy;I. Shames;Mathias Hudoba de Badyn