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...
This Project Grant award, valued at $108,510.00 and awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041), aims to develop a comprehensive solution to provide security and safety-assured industrial control systems (ICS) against malicious cyber-attacks. The project's key objectives are to: 1) develop insightful hybrid automata learning to capture physical invariants and enable system operators to understand detection results, 2) design a real-time provably safe...
The National Science Foundation awarded $314,467 to the University of California, Santa Barbara under the Engineering (47.041) federal grant program to develop an integrated framework for turnkey model predictive control. The project aims to automate the design, model identification, tuning and monitoring of industrial model-based control systems through new techniques to identify process models from measurements. This will enable practitioners to automatically tune control systems for...
This three-year project grant from the National Science Foundation's Engineering Directorate (ENG), funded under the CFDA 47.041 program, provides $299,917 to Wayne State University to develop methodologies for synthesizing cyber-secure and resilient control logic for networked discrete-event systems subject to cyber attacks. The principal objective is to enhance the reliability and performance of advanced control systems that embed complex control logic by providing formal methodologies to...
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...
This $147,212 federal Project Grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) supports research to enable the development of a framework for safe control in autonomous systems in the presence of sensor and actuator faults and cyber attacks. The research combines techniques from control theory, machine learning, and system security to provide provable safety guarantees across a range of autonomous systems and fault/attack scenarios. Key focus areas include...
This $199,964 federal Project Grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) will fund research at the University of California, Santa Barbara (UCSB) to address challenges and opportunities presented by the rapid proliferation of grid-edge resources (GERs) in modern power systems. The project aims to develop novel data-driven control strategies and advance the understanding of GER behavior to ensure the safe and secure operation of these distributed...
This National Science Foundation (NSF) Project Grant award, under the Engineering program (CFDA 47.041), provides $330,130 to North Carolina State University (NC State) from January 1, 2025 to December 31, 2027 to develop new data-driven methodologies for modeling, analyzing, and controlling nonlinear chemical processes. The project aims to create innovative state-space models and machine learning-based techniques to: 1) reconstruct hidden process states from input/output data, 2) analyze...
This $200,000 National Science Foundation Project Grant supports research at Michigan State University to develop data-driven modeling techniques for securing cyber-physical systems. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the two-year award will create automated strategies using reinforcement learning to characterize attacker intent. Researchers will integrate defense approaches combining game theory, reinforcement learning and Bayesian...
This $230,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports research at the University of Connecticut focused on creating a novel computational paradigm called "SMART-RECOVER" to establish resilience against stealthy cyberphysical attacks on digital manufacturing systems. The key research objectives include: (1) pre-fabrication reconstruction of digital geometric models altered by attacks, (2) in-process remodification of...