CPS: Small: Data-Driven Modeling and Control of Human-Cyber-Physical Systems with Extended-Reality-Assisted Interfaces

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Human-cyber-physical systems (h-CPS) are interactive engineered systems that collaborate or interact with one or more human beings to leverage the complementary strengths of both human and autonomy technologies. Medical devices, robot assistive systems, teleoperation, semi-autonomous systems, and other technology-assisted applications are all examples of h-CPS. Because human operations are deeply intertwined with cyber and physical processes in h-CPS, new technical challenges for h-CPS analysis and design emerge, particularly in modeling complex human behaviors, enabling effective human-machine interactions, and developing reliable and high-performance controllers. Furthermore, as envisioned in future h-CPS subject to a large amount of data of adequate quality and quantity available from rich sensing modalities, modeling, interaction, and control procedures are shifting from model-based to data-driven, and new challenges such as trustworthiness and learning efficiency of data-driven methods are expected to arise. This project targets the unique challenges of data-driven modeling and control of h-CPS by developing a holistic data-driven design framework, accounting for addressing today’s major barriers to apply data-driven approaches for modeling, interaction, and control of h-CPS. Educational and outreach activities are well-integrated into the research and include CPS workforce training, interdisciplinary research and curriculum development, and K-12 STEM outreach activities. The designed activities are uniquely positioned to attract members of underrepresented groups with a focus to enhance the diversity of the federal, state, and local CPS workforce.This project will enable the synergistic integration of data-driven modeling and control methods such as neural networks, reinforcement learning, and model-based methods such as hybrid systems, and model predictive control. This project will also explore the benefits of extended reality (XR) in building human-machine interfaces for effective communication and interaction between human users, machines, and environments. Specifically, the research activities in this project will lead to new discoveries of h-CPS in that: (1) a new hybrid learning architecture will be developed for data-driven modeling of human behaviors to accurately capture both human maneuver dynamics and human decision-making processes, (2) XR-assisted interfaces will be developed to interact with semi-or fully-autonomous systems to enhance control capability and performance, and (3) a model-based and data-driven control co-design framework will be developed to enable adaptable and reliable control in human-in-the-plant scenarios, and a co-design framework of outer-loop and inner-loop controllers will be developed to assist human users to augment human control capability. Validation experiments will be conducted using h-CPS testbeds with XR interfaces, such as eye-tracking, gaze-based control, and 3D drone teleoperation systems.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.