Name
Abstract | ||
|---|---|---|
Classical schemes in system identification and adaptive control often rely on persistence of excitation to guarantee parameter convergence, which may be difficult to achieve with a single agent and a single input. Inspired by consensus systems, we extend classical parameter adaptation to the multi agent setting by combining an adaptive gradient law with consensus dynamics. The gradient law represents the main learning signal, while consensus dynamics attract each agent's parameter estimates toward those of its neighbors. We show that the resulting decentralized online parameter estimator can be used to identify the true parameters of all agents, even if no single agent employs a persistently exciting input. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ACC.2014.6858938 | American Control Conference |
Keywords | Field | DocType |
adaptive control,convergence,decentralised control,gradient methods,learning systems,multi-robot systems,parameter estimation,adaptive control,adaptive gradient law,collaborative system identification,consensus dynamics,consensus systems,decentralized online parameter estimator,learning signal,multiagent setting,parameter adaptation,parameter consensus,parameter convergence,parameter identification,Adaptive systems,Identification,Networked control systems | Convergence (routing),Mathematical optimization,Computer science,Control theory,Control engineering,Adaptive control,System identification,Consensus dynamics,Estimator | Conference |
ISSN | Citations | PageRank |
0743-1619 | 3 | 0.41 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivan Papusha | 1 | 3 | 0.41 |
| Eugene Lavretsky | 2 | 3 | 0.41 |
| Richard M. Murray | 3 | 12322 | 1223.70 |