Efficient Algorithms for Optimal Control of Time-Periodic and Nonlinear Systems

流体通常表现出周期性运动，或者是由于外部周期性力（例如，月球潮汐）或由于通过与环境相互作用而自然产生的内力（例如，通过稍微打开的车窗的空气冲击，或者由与桥柱相互作用的河流产生的漩涡）。 这种流动的控制是非常感兴趣的，因为在许多情况下，流动剖面的微小变化可以产生巨大的好处或灾难性的成本。 例如，将循环流型稳定化并调谐到给定频率可以在风力发电场和波浪能转换器的设计中是有用的，而在其他情况下，完全消除振荡运动可以帮助减少施加在关键支撑结构上的疲劳载荷。 该项目通过开发用于仿真和控制的新数学算法来专门解决振荡现象的建模和控制问题，该算法将潜在的周期性系统行为集成到核心建模框架中，从而有望提高精度并降低计算成本。 这项研究将导致对具有周期性行为的系统的数学模型的更好理解，以及新的模拟和建模工具的开发，这将对生物学中的广泛应用产生直接影响（例如，循环和呼吸系统）和能量（例如，风力涡轮机和电网动力学）。周期性流结构的模拟和控制需要专门用于该任务的方法。Floquet变换长期以来一直是解决此类问题的理论工具，它允许系统表示的变化，有效地将周期性时间依赖性从内部动态转移到输入/输出端口。直到最近，它是实用的使用这种方法的小到中等规模的问题。 这项研究的最初重点将是开发可扩展的，数值上有效的算法，用于大规模Floquet变换，使高保真度减少模型随时间变化的周期性流动动力学。通过将算子分裂方法与二次系统的最优模型降阶方法相结合，将开发和分析非线性动力学的输入无关模型降阶的新能力。 这里提出的模型简化框架以适度的成本提供了更高的数值效率和更高的精度。这种方法的基础是将动态的准确表示集成到降阶模型中，更好地尊重底层最优控制问题的属性。 强大的计算工具，帮助模拟和建模的大规模振荡动力学将开发和提供给科学和工程community.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

Fluids often exhibit cyclical motion, either due to external periodic forces (e.g., lunar tides) or due to internal forces that naturally arise through interaction with the environment (e.g., air buffeting through a slightly opened car window or the eddies that develop from a river current interacting with a bridge pillar). The control of such flows is of great interest since in many cases small changes in a flow profile can produce either dramatic benefits or catastrophic costs. Stabilizing and tuning cyclic flow patterns to a given frequency can be useful in the design of wind farms and wave energy converters, for example, while in other circumstances eliminating oscillatory motion altogether may help reduce fatigue loads placed on critical support structures. This project specifically addresses the modeling and control of oscillatory phenomena through the development of new mathematical algorithms for simulation and control that integrate underlying periodic system behavior into the core modeling framework, thus promising both improved accuracy and reduced computational costs. This research will result in improved understanding of mathematical models of systems with periodic behavior as well as the development of new simulation and modeling tools, which will have an immediate bearing on a wide range of applications found in biology (e.g., circulatory and respiratory systems) and energy (e.g., wind turbines and power grid dynamics).Simulation and control of periodic flow structures require methods specialized to the task. The Floquet transformation has long been a theoretical tool for such problems, allowing for a change in system representation that effectively shifts the periodic time dependence out of the internal dynamics into the input/output ports. Only recently has it been practical to use this approach for small to medium scale problems. The initial focus of this research will be on the development of scalable, numerically effective algorithms for large-scale Floquet transformations, enabling the high-fidelity reduced models for time-varying periodic flow dynamics. By combining operator splitting approaches with optimal model reduction methods for quadratic systems, new capabilities for input-independent model reduction for nonlinear dynamics will be developed and analyzed. The model reduction framework that is proposed here offers improved numerical efficiency and greater accuracy at modest cost. Fundamental to this approach will be the integration of an accurate representation of dynamics into reduced-order models, better respecting the properties of the underlying optimal control problem. Robust computational tools aiding the simulation and modeling of large-scale oscillatory dynamics will be developed and provided to the science and engineering community.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.