| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
|
| Initial Amendment Date: | January 16, 2015 |
| Latest Amendment Date: | January 16, 2015 |
| Award Number: | 1454022 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Radhakisan Baheti
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | March 15, 2015 |
| End Date: | February 29, 2020 (Estimated) |
| Total Intended Award Amount: | $500,000.00 |
| Total Awarded Amount to Date: | $500,000.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
526 BRODHEAD AVE BETHLEHEM PA US 18015-3008 (610)758-3021 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
19 Memorial Drive West Bethlehem PA US 18015-3085 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | EPCN-Energy-Power-Ctrl-Netwrks |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Improving energy efficiency as well as robustness to external perturbations in large-scale dynamical networks is critical for long-term sustainability, from engineering infrastructures to living cells. Examples include distributed power networks, distributed emergency response systems, interconnected transportation networks, metabolic pathways, and social and financial networks. The overarching goal of this project is to develop new methodologies to classify viable domain-specific systemic performance, risk, and fragility measures for dynamical networks and quantify inherent fundamental limits on the best achievable values for each class of such systemic measures. The main barrier to achieve this objective is the lack of rigorous knowledge about networks of interconnected dynamical systems when it comes to understanding their structure, dynamics and holistic behaviors.
The issue of fundamental limits and their resulting tradeoffs are important for synthesis of dynamical network as they reveal what is achievable, and conversely what is not achievable by feedback control mechanisms. The focus of this project is to reveal important role of underlying dynamical structure and sparse information structure of dynamical networks in emergence of severe fundamental limits on the best achievable levels of performance and robustness.
This project is highly relevant for analysis and synthesis of engineered and natural dynamical networks. Recent technological advances in distributed control and dynamical systems cannot be efficiently integrated on real platforms without the necessary theoretical and experimental support. This project will produce data-driven algorithms to assess global performance and robustness properties of engineered dynamical networks by computing the values of viable systemic measures in real-time operation. The application areas include energy, robotic, transportation, and biological networks. This project further seeks to make broad impacts through development and dissemination of courses and material in academia and industry that cover key network science concepts while staying grounded in engineering applications.
The technical goal of this project is to develop an integrated theory of dynamical networks based on notions of systemic measures. This project will focus on discovering connections between existing gold standard performance and robustness measures that have been used in disciplines such as control theory, dynamical systems, physics, biology, and finance, and develop a unified systems-theoretic framework for characterization of such measures and their inherent fundamental limits and tradeoffs. This project intends to create a multidisciplinary research environment focusing on developing a foundational science that allows for measuring, predicting, and containing systemic measures for network-oriented applications.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Fundamental limits on computation, prediction, communication, decision, control, energy conversion and even measurement are at the heart of modern mathematical theories of distributed control and dynamical systems. Minimizing resource use as well as robustness to external perturbations in dynamical networks is crucial to sustainability, from engineering infrastructures to living cells. Examples include distributed power networks, distributed emergency response systems, interconnected transportation networks, metabolic pathways, and even social and financial networks. The issue of hard limits and their fundamental tradeoffs in distributed control and dynamical system design lies at the very core of feedback theory since it reveals what is achievable and conversely what is not achievable by distributed feedback control laws.
This project developed new fundamental insights and methodologies to exploit structural properties of large-scale dynamical networks in order to quantify their inherent fundamental limits on performance and robustness and characterize their resulting fundamental tradeoffs. Our approach was based on integration of concepts from dynamical and control systems, optimization, operator, and graph theories. The primary focus of this project was on revealing foundational role of underlying dynamical structure and sparse information structure of large-scale dynamical networks in emergence of severe theoretical fundamental limits on the resulting performance and robustness in such networks.
Intellectual Merit: This research provides a novel unified approach for analysis and synthesis of large-scale dynamical network. The notion of systemic performance and robustness measures provides a unifying umbrella under which an integrated theory of fundamental limits and tradeoffs to systematically deal with uncertainty, performance, robustness, and risk in distributed control and dynamical systems becomes possible. The PI has created a multidisciplinary research environment focusing on developing a foundational science that allows for measuring, predicting, and containing systemic measures for various domain-specific applications.
Broader Impacts: The proposed research is highly relevant for analysis and synthesis of engineered and natural dynamical networks. The PI and his team have developed efficient algorithms to assess global performance and robustness properties of engineered large-scale dynamical networks by computing the value of their corresponding systemic measures in real-time. The application areas include energy, robotic, transportation, and biological networks. The broader impact of this project includes disseminating the research outcomes in various conferences, workshops, and invited seminars.
Last Modified: 06/29/2020
Modified by: Nader Motee
Please report errors in award information by writing to: awardsearch@nsf.gov.
