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

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.