| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
|
| Initial Amendment Date: | January 5, 2018 |
| Latest Amendment Date: | April 11, 2022 |
| Award Number: | 1751356 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Phillip Regalia
pregalia@nsf.gov (703)292-2981 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | February 1, 2018 |
| End Date: | January 31, 2024 (Estimated) |
| Total Intended Award Amount: | $546,275.00 |
| Total Awarded Amount to Date: | $546,275.00 |
| Funds Obligated to Date: |
FY 2020 = $187,493.00 FY 2022 = $93,613.00 |
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
1200 E CALIFORNIA BLVD PASADENA CA US 91125-0001 (626)395-6219 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
1200 E California Blvd Pasadena CA US 91125-0600 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | Comm & Information Foundations |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT 01002223DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Modern technology aims for a massively and diversely connected world populated by a seamless network of intelligent, dynamically distributed systems engaged in a shared interaction with the physical world and each other through unreliable sensors, actuators, and noisy communication channels. Classical information theory, while it has long served as an enabler of high speed, long-distance communication for point-to-point, delay-tolerant communication systems using coding over long blocks of symbols, lacks ready solutions to the new challenges of the era of massive, dynamic connectivity. Evolving networks are rather delay sensitive, so coding over long blocks of observed data will not be feasible. Information exchanges frequently seek to maximizing payoff, rather than simply recover the information sent, and are often event-triggered, prompting new mathematical models and tools to gain insights into jointly optimal sensing/coding/control strategies for these systems. The project will also offer undergraduate research opportunities in conjunction with Caltech's Summer Undergraduate Research Fellowships (SURF) program, alongside outreach activities to middle and high school students via Caltech's Center for Teaching, Learning and Outreach, in order to encourage future scientists and engineers.
The proposed research will advance the state of the art in understanding the fundamental trade-offs between communication and performance in dynamical systems. The project will draw upon the tools from both information theory and control theory to achieve its objectives, which are (1) to establish the fundamental information-theoretic trade-offs in delay-constrained causal source coding for dynamical systems under a distortion constraint; (2) to elucidate the information-theoretic trade-offs of control over noisy channels and propose new coding schemes with theoretical performance guarantees; and (3) to gain insight into jointly optimal sampling and coding strategies for tracking and control.
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.
A dynamical system, for example a drone or a robotic surgeon, is represented by an evolving state, which is a vector of coordinates, velocities and accelerations. The current system state is affected by the past system state and by the control action that is injected into the system. In control with full information, the controller has access to the entire system state vector. In control with partial information, the controller only has access to limited information about the system state. This second scenario is often the case in remotely controlled systems, where the controller is not co-located with the system but must instead communicates with it via a rate-limited communication channel.
The quality of such distributed control systems is gauged by an appropriately defined cost function, which determines how closely the system follows the target, and by the communication rate, which quantifies the amount of data about the system state sent from the observer to the controller. Ideally, one would like to transmit at as low data rate as possible, while achieving a low control cost. Both goals cannot be met at the same time; thus, there is a fundamental rate-cost tradeoff, unsurpassable by any technology.
In several scenarios of interest, the project characterized this fundamental theoretical tradeoff and described nearly-optimal coding strategies. The foundation laid out by the project is crucial both for further developing a comprehensive theory of control over communication channels as well as for designing fast and reliable remote control algorithms.
Last Modified: 06/24/2024
Modified by: Victoria Kostina
Please report errors in award information by writing to: awardsearch@nsf.gov.
