Real-Time Control Co-Design for Reconfigurable Energy-Harvesting Systems

可重构能量收集系统的实时控制协同设计

• 批准号: 2321698
• 负责人: Christopher Vermillion
• 金额: $ 44.81万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321698&HistoricalAwards=false
• 关键词: Real; Time; Control; Co; Design

This grant will fund research that enables renewable energy generation systems, such as wind turbines and kites, to operate optimally over a wide range of environmental conditions, thereby promoting the progress of science and advancing the national prosperity. Environmental variability poses operational challenges for energy-harvesting systems that can be addressed using real-time reconfigurability: simultaneous, on-the-fly changes to both the physical mechanism (plant) and the system control to ensure sustained high efficiency. Offline design methodologies can select optimal plant and control parameters for fixed conditions. No such methodology exists, however, for real-time operation in response to environmental variability and uncertainty, due to critical differences in time scale and effort required to modify plant parameters and control parameters, respectively. This project will fill this knowledge gap by developing an innovative algorithmic framework that accounts for such differences in real-time operation and will further quantify the computational requirements to make real-time reconfigurability worth additional costs and complexity. The PI will leverage engagement with the International Energy Agency Task on Airborne Wind Energy to organize annual workshops on the use of reconfigurable energy-harvesting-system models and open-source software tools created in this project. Student engagement with renewable energy technology will be promoted by implementing a “Physics of Kites” workshop in existing K-12 programming.This research aims to develop the foundations of a receding horizon co-design framework for real-time plant reconfigurability while also addressing fundamental distinctions between plant and control parameters. It accomplishes this outcome by fusing notions from nested co-design and multi-rate hierarchical model predictive control, addressing critical knowledge gaps that arise due to the simultaneous need to (i) consider an economic (rather than tracking) formulation at both levels of the hierarchy, (ii) incorporate surrogate models for tractability, and (iii) consider environmental stochasticity. Specifically, a multi-rate architecture will be investigated whereby a low-order surrogate model is used by the upper-level plant optimization to approximately capture the anticipated behavior of the lower-level control system optimization. An interconnected error system model and small gain framework will be used to address questions of convergence and efficiency under different rates of environmental parameter variation. Finally, recursive Gaussian Process modeling will be used to characterize environmental uncertainty, while reformulating deterministic objective functions into statistical ones, introducing chance constraints, and assessing theoretical properties in a probabilistic sense. The framework will be evaluated through an extensive simulation and scaled experimental validation campaign on an energy-harvesting underwater kite with real-time morphing capability.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.

这笔赠款将资助研究，使可再生能源发电系统，如风力涡轮机和风筝，在广泛的环境条件下以最佳方式运行，从而促进科学进步和国家繁荣。环境变化对能量收集系统提出了运营挑战，这些挑战可以通过实时可重构性来解决：同时对物理机制（工厂）和系统控制进行动态更改，以确保持续的高效率。离线设计方法可以为固定条件选择最佳设备和控制参数。然而，由于分别修改工厂参数和控制参数所需的时间尺度和工作量的关键差异，对于响应环境变化和不确定性的实时操作，不存在这样的方法。该项目将通过开发一个创新的算法框架来填补这一知识空白，该框架将考虑到实时操作中的这种差异，并将进一步量化计算要求，以使实时可重构性值得额外的成本和复杂性。PI将利用与国际能源署空中风能任务的合作，组织关于使用该项目中创建的可重构能量收集系统模型和开源软件工具的年度研讨会。通过在现有的K-12课程中开设“风筝物理学”研讨会，促进学生参与可再生能源技术。本研究旨在为实时工厂可重构性建立一个后退式协同设计框架的基础，同时解决工厂和控制参数之间的根本区别。它通过融合嵌套协同设计和多速率分层模型预测控制的概念来实现这一结果，解决了由于同时需要（i）在两个层次上考虑经济（而不是跟踪）配方而产生的关键知识缺口，（ii）将替代模型用于易处理性，以及（iii）考虑环境随机性。具体而言，多速率架构将被调查，其中低阶代理模型被用于上层工厂优化，以近似地捕捉预期的行为的较低级别的控制系统优化。一个相互关联的误差系统模型和小增益框架将用于解决在不同的环境参数变化率下的收敛和效率问题。最后，递归高斯过程建模将用于表征环境的不确定性，同时将确定性目标函数重新表述为统计目标函数，引入机会约束，并在概率意义上评估理论属性。该框架将通过对具有实时变形能力的能量收集水下风筝进行广泛的模拟和缩放实验验证活动进行评估。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。