Wei RenProfessorElectrical & Computer Eng Dept weiren@ucr.edu(951) 827-6204
Robust Distributed Average Tracking for Networked Dynamical Systems
|
AWARD NUMBER
007647-002
FUND NUMBER
33178
STATUS
Closed
AWARD TYPE
3-Grant
|
AWARD EXECUTION DATE
8/10/2015
BEGIN DATE
10/1/2015
END DATE
9/30/2018
AWARD AMOUNT
$238,829
|
Sponsor Information
SPONSOR AWARD NUMBER
SPONSOR
SPONSOR TYPE
FUNCTION
Organized Research
PROGRAM NAME
Proposal Information
PROPOSAL NUMBER
14030232
PROPOSAL TYPE
New
ACTIVITY TYPE
Basic Research
PI Information
PI
Ren, Wei
PI TITLE
Other
PI DEPTARTMENT
Electrical & Computer Eng
PI COLLEGE/SCHOOL
Bourns College of Engineering
CO PIs
Project Information
ABSTRACT
Many tasks performed by distributed dynamic systems can be thought of as occurring on a network of dynamic nodes interconnected by communication links. These tasks include sensing, estimation, control, and optimization by distributed mobile agents, such as vehicles. Implementation of these tasks often reduces to computation of the average, or weighted average, of some variable defined at each network node. Because communication across network links may be slow or expensive, it is important to spread the effort of computing this average across all the networked systems. This motivates the development of "distributed algorithms" that rely only on information from immediate neighbors, that is, from systems that can directly communicate with each other. While great progress has been made in distributed averaging algorithms, these rely on highly simplifying assumptions. This project will enable effective distributing averaging on a range of realistic systems, and experimentally validate the results on a network of robots. The result will apply to numerous open, physically relevant, problems.
Existing distributed averaging methods rely primarily on linear local repeated averaging-type or consensus-type algorithms. These can only deal with prescribed cases, such as averaging initial conditions, or steady signals, or Laplace transformed quantities. Hence their applicability to practical applications is limited. The objective of this project is to derive a robust distributed average tracking framework based on novel nonsmooth nonlinear algorithms to enable distributed coordination of networked systems. The project will address robust distributed average tracking accounting for measurement and communication noise, different agents' varying partial observability and discrepant data quality, inherent physical dynamics, and optimization objectives. The results will fill in the gap in the distributed averaging paradigm to benefit many civilian, homeland security, and military applications involving networked systems.(Abstract from NSF)
|