Project Grant FA95502410102

This Project Grant from the National Science Foundation's $200,000 Engineering program (CFDA 47.041) will fund research at the University of California, San Diego to develop a new data-driven power systems control framework with stability guarantees. The three-year award beginning March 2022 aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability through a novel approach bridging Lyapunov control theory and...

This three-year, $375,000 National Science Foundation project grant supports research at the University of Illinois to develop distributionally robust adaptive control methodologies for reinforcement learning algorithms. The goal is to enable safe and robust autonomous operation of complex systems by constructing a new class of adaptive controllers that are robust to errors in learned distributions. This will allow reinforcement learning algorithms to directly interact with the controllers...

This Project Grant award from the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems supports research to develop low-complexity, safe learning-enabled algorithms for partially observable nonlinear systems with uncertain dynamics. The $400,000 award to Michigan State University aims to accomplish two key objectives: 1) Propose direct data-driven learning approaches for backup safe control policies in partially observable nonlinear systems, and 2) Introduce...

This three-year $300,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering (47.041) federal grant program, funds research at New York University to advance the mathematical foundations and develop new tools for real-time distributed optimization-based control of large-scale nonlinear uncertain systems. Specifically, the award supports three research tasks: 1) synthesizing distributed optimization algorithms robust...

This three-year, $240,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will support collaborative research between Columbia University and the University of Pennsylvania on scalable and communication-efficient learning-based distributed control. The researchers will develop a foundational and integrated theory of distributed learning-enabled control and approximated distributed...

This $200,000 National Science Foundation project grant supports the development of data-driven power systems control with stability guarantees. Funded through the NSF Engineering program (CFDA 47.041), the award to Carnegie Mellon University will support three thrusts of collaborative research over a 30-month period ending February 2025. The research aims to design a new framework integrating reinforcement learning algorithms with Lyapunov stability theory to provide stability guarantees for...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award of $600,000 to the Massachusetts Institute of Technology (MIT) supports research into developing better algorithms for machine learning problems that involve sequential data with rich dependency structures. The project will explore learning methods for linear dynamical systems, graphical models, and hidden Markov models, with the goal of proving rigorous theoretical...

This $250,000 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to address challenges in stochastic nonlinear control and learning for dynamical systems through a novel "Spectral Dynamic Embedding" approach. Led by the Georgia Tech Research Corporation, the research intends to develop computationally efficient control algorithms suitable for applications in robotics, aerospace, manufacturing, and beyond. The key innovations involve...

This three-year $240,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will support research to advance the practical application of distributed learning-enabled control systems. The University of Pennsylvania and Columbia University will collaborate on developing foundational theory and integrated approaches for scalable and communication-efficient distributed control. This includes...

This $166,110 project grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences supports theoretical and computational research on the design, analysis, and implementation of control systems for complex stochastic dynamical systems. The 3-year project, which commenced on June 1, 2024, is a collaborative effort between US-based and UK-based investigators from the University of Connecticut. The key objectives of the work include: (1) developing feedback control...