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.

这项赠款将资助能够在各种环境条件下最佳运营的可再生能源产生系统（例如风力涡轮机和风筝）的研究，从而促进科学的进步并促进国家繁荣。环境变异性对能源收获系统构成了运营挑战，可以使用实时重构性来解决：简单的，即时更改物理机制（工厂）和系统控制，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植物和控制参数。但是，由于分别修改植物参数和控制参数所需的时间尺度和努力，因此，对于响应环境变异性和不确定性而进行实时操作尚无这种方法。该项目将通过开发一个创新的算法框架来填补这一知识差距，该算法框架构成了实时操作的这种差异，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将利用与国际能源机构有关机载风能的任务的参与，以组织该项目中创建的可重新配置的能源收获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编程中实施“风筝物理”研讨会，将促进学生参与可再生能源技术的参与。本研究旨在开发用于实时植物重构植物可重构框架的退化地平线共同设计框架的基础，同时还解决植物和控制参数之间的基本区别。它通过融合嵌套的共同设计和多速率层次模型预测控制的注释来实现这一结果，从而解决了由于同时需要（i）考虑在两个级别的层次级别上制定的经济（而不是跟踪）引起的关键知识差距，（ii）将替代模型纳入了tractabority和（iiiiii）的环境，并考虑了环境的构图。具体而言，将研究多速率体系结构，从而将低阶替代模型由高级工厂优化使用，以近似捕获下层控制系统优化的预期行为。互连的错误系统模型和少量增益框架将用于解决不同环境参数变化速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不确定性，同时将确定性的目标功能改革为统计函数，引入机会限制，并以概率意义评估理论特性。该框架将通过广泛的模拟和扩展的实验验证活动来评估，并具有实时变形能力的能源收获水下风筝。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估标准通过评估来评估的。