CAREER: Physics-Infused Reduced-Order Modeling for Control Co-Design of Morphing Aerial Autonomous Systems

该学院早期职业发展计划(CALEAR)奖支持研究，使下一代自主系统的动态建模、控制和设计具有更强的能力和运营效率，从而促进科学进步，促进繁荣和福利，并确保国防安全。变形自主航空系统，改变飞行中的物理配置，与非变形系统相比，提供更高的机动性、能效、耐用性和任务多功能性。将采取同时考虑自动驾驶功能和车辆设计的控制联合设计方法。然而，由于高维设计空间的存在，加入变形车辆建模、设计和操作所需的高保真模型带来了计算难度的挑战。该项目寻求弥合创建复杂模型和保持计算实用性之间的差距，为更高效的设计过程铺平道路。这项研究将建立一个基础性的框架，可以彻底改变自主系统从生产到运营的整个生命周期，包括随着时间的推移学习和适应的能力。除了技术进步，该项目致力于通过综合教学-学习-研究活动提供的全面培训和研究机会，以及通过高级顶峰项目提供的综合实践经验，培养一支强大的工程劳动力队伍。此外，通过广泛数字化和共享教育资源，这一倡议还旨在丰富全国各机构的工程教育。该项目旨在通过系统集成高保真、实时模型的创新建模和优化技术，推动未来自主系统控制共同设计的范式转变。首先，这项研究将提出一种降维建模方法和一种新的物理数据输入形式，该方法耦合了由基于物理的组件和数据驱动的组件组成的动态系统，以支持无限维参数，同时保持计算的可处理性和高的模型保真度。其次，这项研究将解决变形飞行器应用中的建模难题，涉及流体、结构、飞行和变形的耦合动力学，在保持适合于硬件实时控制的计算速度的同时，以前所未有的模型保真度。第三，算法将解决控制联合设计问题，作为一个高度非线性、高维、非凸、多目标优化问题，系统的案例研究将在控制联合设计的原则和指南方面产生新的知识。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 Program (CAREER) award supports research that enables the dynamic modeling, control, and design for next-generation autonomous systems with enhanced capabilities and operational efficiency, thereby promoting the progress of science, advancing prosperity and welfare, and securing the national defense. Morphing autonomous aerial systems, changing physical configurations in-flight, deliver increased maneuverability, energy efficiency, durability, and task versatility compared to their non-morphing counterparts. A control co-design approach that simultaneously considers the autonomous functionalities and vehicle design will be undertaken. However, incorporating high-fidelity models, required by modeling, design and operation of morphing vehicles, brings computational intractability challenges due to the high-dimensional design space. This project seeks to bridge the gap between creating sophisticated models and maintaining computational practicality, paving the way for more efficient design processes. This research will establish a foundational framework that could revolutionize the entire lifecycle of autonomous systems, from production to operation, including the ability to learn and adapt over time. Beyond technological advancements, the project is dedicated to fostering a strong workforce in engineering through comprehensive training and research opportunities provided by integrated teaching-learning-research activities and integrative praxis experience via senior capstone projects. Furthermore, by digitizing and sharing educational resources widely, this initiative also aims to enrich engineering education across various institutions nationwide. This project aims to prompt a paradigm shift in the control co-design of future autonomous systems via innovative modeling and optimization techniques that systematically integrate high-fidelity, real-time models. First, this research will contribute a reduced-order modeling method with a novel physics-data infusion formalism that couples dynamical systems consisting of both physics-based and data-driven components to support infinite dimensional parameters while maintaining computational tractability and high model fidelity. Second, this research will resolve the modeling dilemma in the application of a morphing vehicle involving coupled dynamics of fluids, structures, flight, and morphing, at an unprecedented model fidelity while maintaining a computational speed suitable for real-time control on hardware. Third, the algorithms will address the control co-design problem, as a highly nonlinear, high-dimensional, non-convex, multi-objective optimization, and the systematic case study shall generate new knowledge in the principles and guidelines of control co-design.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.