灵敏度驱动的试验设计，以便于控制动力系统
Sensitivity driven experimental design to facilitate control of dynamical systems
摘要：
非线性动力系统的控制是一个复杂的多方面的过程。许多工程系统的基本要素包括高保真度的基于物理的建模、离线轨迹规划、反馈控制设计以及用于减少不确定性的数据采集策略。本文提出了一个以优化为中心的观点，它将这些元素耦合到一个内聚框架中。我们介绍了一种新的使用超微分灵敏度分析，以了解反馈控制器的灵敏度参数不确定性的物理模型用于轨迹规划。这些灵敏度提供了一个基础，以定义一个最佳的试验设计，试图获得数据最相关的减少对反馈控制器的需求。我们提出的框架说明Zermelo导航问题和高超音速轨迹控制问题，使用NASA的X-43高超音速飞行试验的数据。
英文摘要：
Control of nonlinear dynamical systems is a complex and multifaceted process. Essential elements of many engineering systems include high fidelity physics-based modeling, offline trajectory planning, feedback control design, and data acquisition strategies to reduce uncertainties. This article proposes an optimization centric perspective which couples these elements in a cohesive framework. We introduce a novel use of hyper-differential sensitivity analysis to understand the sensitivity of feedback controllers to parametric uncertainty in physics-based models used for trajectory planning. These sensitivities provide a foundation to define an optimal experimental design which seeks to acquire data most relevant in reducing demand on the feedback controller. Our proposed framework is illustrated on the Zermelo navigation problem and a hypersonic trajectory control problem using data from NASA's X-43 hypersonic flight tests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
