CAREER: Toward Real-Time, Constraint-Aware Control of Complex Dynamical Systems: from Theory and Algorithms to Software Tools

这笔学院早期职业发展(Career)补助金将资助研究，促进实时自动控制知识的发展，并将其应用于电力、交通和制造业基础设施中复杂的、安全关键的系统，从而促进科学进步，促进国家繁荣。复杂系统的特点是多个子系统的耦合、未建模的组件、显著的可变性和环境的不确定性，这阻碍了传统的基于模型的技术的有效使用，并使得当前的数据驱动方法在计算上难以处理。这个项目通过建立一个新的理论框架来解决这些缺点，以创建基于数据的算法，这些算法易于由非专家调整，实时可行，并伴随着安全和性能的保证。一项全面的教学和推广计划旨在整合佛蒙特州的自主技术生态系统，建立和加强产业、学术和政府之间的关系，通过共享开源软件和可访问的视频内容来传播研究成果，并与来自代表不足和服务不足的社区的学生接触，包括佛蒙特州的农村高中。这项研究旨在为新的数据驱动的复杂系统控制框架奠定基础，该框架通过将基于内部模型控制和参考州长模型的设计技术与行为系统理论的数据驱动方法相结合来分离跟踪和约束管理的问题。通过继承基于模型的方法的许多理想的性质，例如稳健的稳定性、约束执行和有限时间收敛到约束允许的设定点，该框架克服了数据驱动方法在模块化结构中的局限性，简化了分析和设计，并允许容易调整的、计算上容易处理的算法。为此，该项目将研究新控制框架的系统理论基础，表征其性质和基本限制，并将其推广到非线性、时变和不稳定系统。在与Beta Technologies和佛蒙特州电力公司(Vermont Electric Power Company，VELCO)的合作下，该控制框架将在电机实验、运输复杂有效载荷的四旋翼群以及具有大量分布式能源渗透的电力系统的硬件在环仿真中得到验证。该项目由动力学、控制和系统诊断计划和既定的激励竞争研究计划(EPSCoR)共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) grant will fund research that advances knowledge of real-time automatic control with application to complex, safety-critical systems in power, transportation, and manufacturing infrastructure, thereby promoting the progress of science, and advancing the national prosperity. Complex systems are characterized by the coupling of many subsystems, unmodeled components, significant variability, and environmental uncertainty, which prevent effective use of traditional model-based techniques, and render current data-driven approaches computationally intractable. This project addresses these shortcomings by building a new theoretical framework for creating data-based algorithms that are easy to tune by non-experts, real-time feasible, and accompanied by guarantees of safety and performance. A comprehensive plan for pedagogy and outreach aims to integrate the autonomous technology ecosystem in Vermont, establish and strengthen relationships between industry, academia, and government, disseminate research results through sharing of open-source software and accessible video content, and engage with students from underrepresented and underserved communities, including at rural high schools in Vermont.This research aims to develop the foundations for a new data-driven control framework for complex systems, which decouples the problems of tracking and constraint management through a marriage of the Internal Model Control and Reference Governor model-based design techniques with the Behavioral Systems Theory data-driven approach. By inheriting many desirable properties of the model-based approaches, such as robust stability, constraint enforcement, and finite-time convergence to constraint-admissible setpoints, this framework overcomes the limitations of data-driven approaches within a modular structure that simplifies analysis and design, and allows for easily tunable, computationally tractable algorithms. To this end, the project will investigate the system-theoretic underpinnings of the new control framework, characterize its properties and fundamental limitations, and develop generalizations to nonlinear, time-varying, and unstable systems. Informed by collaboration with Beta Technologies and the Vermont Electric Power Company, VELCO, the control framework will be validated on an electric motor experiment, a quadrotor swarm transporting a complex payload, and a hardware-in-the-loop simulation of a power system with significant penetration of distributed energy resources. This project is jointly funded by the Dynamics, Control and Systems Diagnostics program and the Established Program to Stimulate Competitive Research (EPSCoR).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.