Collaborative Research: Scalable & Communication Efficient Learning-Based Distributed Control

智能基础设施（例如，交通、能源）将在社会向更可持续的未来过渡的过程中发挥不可或缺的作用。这些系统必须在不确定和动态的环境中可靠、鲁棒和高效地运行。反馈控制是提供这种保证的使能技术。集中式控制是一种成熟的技术，其中一个系统由单个决策者控制，具有成熟的理论和高效的算法，并且在许多应用中取得了工程成功，例如商业航空，过程控制和机器人。相比之下，分布式控制，其中多个子系统由多个决策者控制，更具挑战性。虽然在过去的十年中产生了丰富的新理论和计算工具来解决分布式控制问题，但人们仍然注意到，分布式控制在智能基础设施等新兴领域的实际影响仍然很小。 该项目旨在解决这一问题，并通过构建分布式学习控制和近似分布式优化的基础和集成理论，将分布式控制从理论转移到实践中。 在教育方面，该项目的研究成果将被纳入研究生水平的学习控制（宾夕法尼亚大学）和分布式优化（哥伦比亚）课程。 从长远来看，该项目旨在创建一个新的研究人员社区，致力于分布式学习，控制和优化的交叉点，并将利用部门的努力为该项目招募一批不同的博士生。该项目的动机是观察到安全约束实时分布式控制的实际应用仍然存在重大障碍：（i）现有方法对于实时控制来说太慢;（ii）分布式最优控制主要集中在线性模型上，而许多感兴趣的系统是非线性的;以及（iii）通常假设可以获得反映系统拓扑结构的高质量结构化模型。推力将覆盖整个控制工程管道，以解决这些差距。在第一个目标中，联邦学习和统计学习被纳入结构化系统识别中。Thrust II旨在通过开发分布式模仿和联邦学习工具来加速分布式预测控制。最后，推力III集中在分布式控制器的设计鲁棒性的不确定性学习，数值方法，和通信故障。与现有的工作相比，该建议提供了第一个集成的方法来设计控制器，可以实际部署到社会规模的系统。将在宾夕法尼亚大学的机器人平台和哥伦比亚的HBT-EP等离子体聚变托卡马克装置的数据上对所开发的方法进行实验验证。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知识价值和更广泛的影响审查标准进行评估来支持。

Intelligent infrastructures (e.g., transportation, energy) are poised to play an integral role in the ongoing societal transition towards a more sustainable future. These systems must operate reliably, robustly, and efficiently in uncertain and dynamic environments. Feedback control is the enabling technology for providing such guarantees. Centralized control, where one system is controlled by a single decision maker, is a mature technology with well-developed theory and efficient algorithms, and has enabled engineering successes across many applications, such as commercial aviation, process control, and robotics. In contrast, distributed control, wherein multiple subsystems are controlled by multiple decision makers, is much more challenging. While the last ten years have produced a wealth of new theory and computational tools for addressing the distributed control problem, it is nevertheless observed that the practical impact of distributed control in emerging areas such as smart infrastructure remains minimal. This project seeks to address this issue and move distributed control from theory to practice by building a foundational and integrated theory of distributed learning-enabled control and approximated distributed optimization. On the educational front, the research outcomes of this project will be integrated into graduate-level courses on learning-enabled control (Penn) and distributed-optimization (Columbia). Longer term, this project aims to create a new community of researchers working at the intersection of distributed learning, control, and optimization, and departmental efforts will be leveraged to recruit a diverse group of PhD students for the project.This project is motivated by the observation that there remain significant barriers to the practical use of safety-constrained real-time distributed control: (i) Existing methods are much too slow for real-time control; (ii) Distributed optimal control has mainly focused on linear models while many systems of interest are nonlinear; and (iii) It is often assumed that high-quality structured models reflecting system topology are available. Thrusts will cover the full control engineering pipeline to address these gaps. In Thrust I, federated and statistical learning are incorporated into structured system identification. Thrust II seeks to speed up distributed predictive control by developing distributed imitation and federated learning tools. Finally, Thrust III focusses on the design of distributed controllers robust to uncertainty from learning, numerical methods, and communication failures. In contrast to existing work, this proposal offers the first integrated approach to designing controllers that can be realistically deployed to societal-scale systems. Experimental validation of developed methods will be conducted on robotic platforms at Penn and on data from an HBT-EP plasma fusion tokamak at Columbia.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.