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（Career）奖支持能够为下一代自主系统提供动态建模、控制和设计的研究，这些系统具有增强的能力和运行效率，从而促进科学进步，促进繁荣和福利，并确保国防安全。与非变形系统相比，变形自主空中系统可以改变飞行中的物理配置，提高机动性、能源效率、耐用性和任务多功能性。同时考虑自动驾驶功能和车辆设计的控制协同设计方法将被采用。然而，变形车辆的建模、设计和运行需要采用高保真模型，由于设计空间的高维性，给计算带来了难题。该项目旨在弥合创建复杂模型和保持计算实用性之间的差距，为更有效的设计过程铺平道路。这项研究将建立一个基础框架，可以彻底改变自主系统的整个生命周期，从生产到运行，包括学习和适应能力。除了技术进步，该项目还致力于通过综合教学研究活动和高级顶点项目的综合实践经验，提供全面的培训和研究机会，培养强大的工程人才。此外，通过数字化和广泛共享教育资源，该计划还旨在丰富全国各院校的工程教育。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，促进未来自主系统控制协同设计的范式转变。首先，本研究将提供一种降阶建模方法，该方法采用一种新颖的物理数据注入形式，将基于物理和数据驱动的组件组成的动力系统耦合在一起，以支持无限维参数，同时保持计算可追溯性和高模型保真度。其次，本研究将以前所未有的模型保真度，同时保持适合硬件实时控制的计算速度，解决变形飞行器应用中涉及流体、结构、飞行和变形耦合动力学的建模难题。第三，算法将解决控制协同设计问题，作为一个高度非线性、高维、非凸、多目标的优化问题，系统的案例研究将在控制协同设计的原理和指导方针方面产生新的知识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.