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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:REGENTS OF THE UNIVERSITY OF MINNESOTA
Doing Business As Name:University of Minnesota-Twin Cities
PD/PI:
  • Donatello Materassi
  • (612) 625-4873
  • mater013@umn.edu
Award Date:04/08/2020
Estimated Total Award Amount: $ 344,623
Funds Obligated to Date: $ 344,623
  • FY 2018=$93,612
  • FY 2019=$243,028
  • FY 2016=$7,983
Start Date:06/06/2019
End Date:07/31/2021
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CAREER: Design of in-line controllers for continuously operating networks with structural uncertainty
Federal Award ID Number:2000302
DUNS ID:555917996
Parent DUNS ID:117178941
Program:CPS-Cyber-Physical Systems
Program Officer:
  • Ralph Wachter
  • (703) 292-8950
  • rwachter@nsf.gov

Awardee Location

Street:200 OAK ST SE
City:Minneapolis
State:MN
ZIP:55455-2070
County:Minneapolis
Country:US
Awardee Cong. District:05

Primary Place of Performance

Organization Name:University of Minnesota-Twin Cities
Street:
City:
State:MN
ZIP:55455-2070
County:Minneapolis
Country:US
Cong. District:05

Abstract at Time of Award

This project focuses on designing control mechanisms for a networked system with unknown structure by making use only of non-invasive observations. By non-invasive observations, it is meant that what is being measured is not the system reaction to actively injected inputs, but rather the system behavior when it is operating under standard conditions and subject to potentially unobservable forcing signals. The capability of designing controllers based only on non-invasive observations is of paramount importance for any large scale network fulfilling critical or uninterruptible functions (i.e., a power grid, a logistic system) or in situations where it is impractical or too expensive to inject known probing signals into the system (i.e., a gene network, a financial network). Other relevant applications are in medicine (i.e., repeated drug testing, computer- assisted anesthesia). Indeed, in these cases, for obvious safety and health concerns, it is not desirable to actively test the response of a patient to a different drug dosage or treatment, if comparably useful information could be inferred from non-invasive observations. Since non-invasive observations do not always provide full information about the network's configuration, the project will also consider how to define adequate control mechanisms that are robust with respect to uncertainties in the connectivity structure. These kinds of uncertainties are not typically considered in standard techniques for control design and the development of specific methodologies is required. Combined with the capability of adapting to changes in the network's configuration, these control techniques will provide a solid foundation for the realization of a self-healing system. This project will bridge together different areas, including statistics, computer science, and control theory with a single unifying framework. New courses will be created to facilitate communication among all these communities of researchers, advancing separate fields in a multidisciplinary way.

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