This $600,000 Project Grant was awarded on December 15, 2024 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to the Regents of the University of Michigan. The project aims to develop a comprehensive framework for end-to-end verification of control systems, from high-level hybrid models down to embedded C code implementation. Key research objectives include bridging the gap between control theory and low-level system...
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)...
This National Science Foundation (NSF) award under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $175,000 in funding to the Regents of the University of Michigan, doing business as the University of Michigan-Dearborn, for a 2-year project from March 2024 to February 2026.
The project aims to develop innovative model-based verification techniques to analyze the safety, reliability, and accuracy of cyber-physical systems (CPS)...
This $199,867 National Science Foundation project grant supports research at the University of Michigan to develop formal methodologies for synthesizing cyber-secure and resilient control logic for networked discrete-event systems subject to cyber attacks. Funded under the NSF Engineering program (CFDA 47.041), the three-year project commencing September 2022 aims to enhance the reliability of advanced control systems through model-based approaches. Researchers will extend diagnosability...
The National Science Foundation (NSF) awarded a $213,679 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the University of California, Berkeley. This grant, titled "CAREER: HIGH-ASSURANCE DESIGN OF LEARNING-ENABLED CYBER-PHYSICAL SYSTEMS WITH DEEP CONTRACTS", aims to develop a novel methodology for the design and verification of learning-enabled cyber-physical systems (CPS). The project will pursue a compositional...
This $147,212 federal Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research to develop a framework for ensuring safe control of autonomous systems, such as driverless cars and robotic systems, in the presence of sensor faults and cyber attacks. The research combines techniques from control theory, machine learning, and system security to enable provable safety guarantees for learning-enabled autonomous systems across a range of...
This $300,000 National Science Foundation project grant supports research into distributed optimization-based control of large-scale nonlinear systems with uncertainties from 2022-2025. Funded under the NSF Engineering program (CFDA 47.041), the award to the University of California, San Diego will advance mathematical foundations for distributed optimization algorithms robust to uncertainties. Researchers will design tracking controllers for local systems to follow optimization-derived...
This $300,000 National Science Foundation (NSF) Engineering (CFDA 47.041) Project Grant award to President and Fellows of Harvard College aims to develop efficient and reliable control algorithms for stochastic nonlinear dynamical systems. The research will introduce a novel "Spectral Dynamic Embedding" approach to reformulate nonlinear system dynamics in a way that enables tractable optimization and rigorous analysis of optimal control policies. Potential applications include...
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 $149,343 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program supports the development of a new quantitative verification approach for temporal properties of learning-enabled cyber-physical systems (LE-CPS). The key objectives are to:
Develop a qualitative and quantitative verification approach for LE-CPS at the system level based on Probstar reachability, providing the precise probability...