.png){kind=icon}
.png){kind=icon}
Return
Event-triggered distributed optimization for multi-agent systems with quantization and disturbance rejection
.png){kind=icon}
PRE
AI
DOI：10.1093/imamci/dnag001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, adaptive continuous-time algorithms with event-triggered mechanism are studied to solve the optimization problem. First, an event-triggered adaptive algorithm is introduced, and it is proven that this algorithm can effectively solve the optimization problem. Second, to solve the optimization problem, two event-triggered algorithms are proposed, one considering uniform quantization information and the other addressing external disturbances. It is demonstrated that the states of the multi-agent systems practically converge to the global optimal point. Three numerical cases demonstrate the effectiveness of the relevant results.
Keywords:
optimization problem
event-triggered mechanism
uniform quantization
external disturbances
Journal
I
IF:
1
Papers:
24
Citations:
0