Project Grant 2338749

Award Date 9/1/24
Completion Date 8/31/29
Dollars Obligated $655K
Federal Grant Program
47.041
Assistance Type
Project Grant
Place of Performance
Houston, TX 77204, USA
Similar Awards
This National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program award provides $581,320 in funding to the University of Vermont (UVM) over a 5-year period from June 1, 2024 to May 31, 2029. The research project, titled "A Universal Framework for Safety-Aware Data-Driven Control and Estimation", aims to develop a framework for the simultaneous design of control policies and safety measures for complex systems like robotics and power systems using data-driven...
The National Science Foundation awarded a $477,423 Project Grant to the University of California, San Diego under the Engineering program (CFDA 47.041) to support research titled "CAREER: NONSMOOTH CONTROL SYSTEMS FOR SOCIETAL NETWORKS WITH DATA-ASSISTED FEEDBACK LOOPS: THEORY AND ALGORITHMS." The research aims to advance the analysis and synthesis of hybrid and non-smooth data-assisted controllers for multi-agent systems deployed over cyber-physical infrastructure. Specific objectives...
The National Science Foundation awarded a $400,000 Project Grant to the University of Washington under the Engineering program (CFDA 47.041) to develop data-guided system-theoretic techniques for control of dynamic systems from August 15, 2022 to July 31, 2025. Specifically, the award will support research to extend the current data-guided control paradigm to nonlinear systems and networked systems through novel data-parameterized analysis and synthesis methods. The research will also examine...
This Project Grant award for $108,510, provided by the National Science Foundation (CFDA 47.041 - Engineering), aims to develop a comprehensive solution to protect industrial control systems (ICS) from malicious cyber-attacks. The key products and services to be delivered include: Developing an "operator-automation shared protection framework" that integrates human-on-the-loop explainable machine learning, anomaly detection, and recovery control to enable real-time decision-making...
The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $500,000 Project Grant to Texas A&M Engineering Experiment Station, doing business as Tees, to support research towards a principled framework for resilient, data efficient and scalable reinforcement learning for control. The award period is from February 1, 2021 through January 31, 2026. The research is funded under the NSF Directorate for Engineering's Engineering program (CFDA 47.041), which...
The National Science Foundation awarded a $567,426 Project Grant to The Regents of the University of Colorado under the Engineering federal grant program (CFDA 47.041). The grant will support research titled "CAREER: HYPERSAMPLED MODEL PREDICTIVE CONTROL: RECONCILING ANALOG SYSTEMS WITH DIGITAL CONTROLLERS" from August 2021 through July 2026. The research aims to develop new control strategies that reconcile analog physical systems with digital controllers by using model predictive...
The National Science Foundation (NSF) Engineering program awarded a 5-year, $589,527 Faculty Early Career Development (CAREER) grant to the University of Texas at Dallas (UTD) on June 1, 2024. The grant supports research to develop set-based dynamic modeling and control frameworks for improving the safety and reliability of thermal management systems in complex energy systems. Key objectives include: Create set-based modeling and control techniques to account for the uncertain behaviors of...
The National Science Foundation (NSF) Directorate for Engineering (CFDA Program 47.041) awarded a $344,126 Project Grant to the University of Wisconsin System for a 3-year research project focused on developing new mathematical methods and computational tools to enable the use of complex data formats, such as visual and thermal images, for advanced model predictive control (MPC) systems. The key objectives are to: Integrate concepts from control theory, topology, machine learning, and Bayesian...
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...
The University of Houston received a five-year, $431,679 Project Grant from the National Science Foundation Division of Chemical, Bioengineering, Environmental, and Transport Systems under the Engineering program (CFDA 47.041). The grant funds the CAREER: NEUROMUSCULAR COORDINATION (NEUROCOORD)-GUIDED HUMAN-MACHINE INTERACTION FOR QUANTIFYING AND IMPROVING MOTOR FUNCTION AFTER STROKE project. The project aims to develop new human-machine interaction technologies using neurocoordination...

This National Science Foundation (NSF) Engineering (CFDA 47.041) grant award, titled "CAREER: DATA-ENABLED NEURAL MULTI-STEP PREDICTIVE CONTROL (DEMUSPC): A LEARNING-BASED PREDICTIVE AND ADAPTIVE CONTROL APPROACH FOR COMPLEX NONLINEAR SYSTEMS", provides $655,248 in funding to the University of Houston System from September 2024 through August 2029. The project aims to conduct fundamental research to develop data-driven and learning-based predictive and adaptive control approaches, with a focus on applications in healthcare, biomedical, advanced manufacturing, chemical, and automotive industries. Key objectives include enabling new knowledge related to data-enabled automatic control for complex, changing processes, and integrating this research with educational and outreach activities to broaden participation in control research. The project will be led by researchers at the University of Houston.

Generated 10/29/24, 3:34 AM