Name
Title | ||
|---|---|---|
Event-triggered cooperative learning from output feedback control for multi-agent systems. |
Abstract | ||
|---|---|---|
In this paper, the event-triggered distributed cooperative learning from output feedback control is presented for a group of uncertain nonlinear systems whose structures are identical but their reference signals are different. An event-triggered communication scheme is used in the control process to overcome the disadvantages of continuous communication. Meanwhile, the weight estimates of all neural networks (NNs) also converge to a small neighborhood of their optimal values, and the generalization ability of NNs is well guaranteed. Specifically, the trigger condition of each agent is only dependent on its own NN weight estimate. It is proved that the inter-event times are lower bounded by a positive constant to avoid the accumulation of events. Finally, a numerical example is provided to substantiate the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.09.058 | Neurocomputing |
Keywords | Field | DocType |
Event-triggered communication,Output feedback control,Neural networks,Distributed cooperative learning | Nonlinear system,Multi-agent system,Event triggered,Artificial intelligence,Cooperative learning,Artificial neural network,Mathematics,Machine learning,Bounded function | Journal |
Volume | ISSN | Citations |
322 | 0925-2312 | 1 |
PageRank | References | Authors |
0.34 | 25 | 4 |