.png){kind=icon}
.png){kind=icon}
Return
Distributed Quantized Optimization Design of Continuous-Time Multiagent Systems Over Switching Graphs
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2020.2966636.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article focuses on the distributed quantized optimization problem of continuous-time multiagent systems (MASs) over switching graphs. By proposing a dynamic encoding-decoding scheme, a distributed protocol via sampled and quantized data is developed, which can obtain an exact optimal solution, rather than an approximate optimal solution. Compared with existing works on quantized distributed optimization of MASs, the protocol presented in this article does not require the global information on the communication graph or the initial state. Besides, in this article, the gradients of the cost functions are not required to be bounded functions. A simulation example is finally presented to illustrate the effectiveness of the proposed distributed protocol.
Keywords:
Switches
Protocols
Multi-agent systems
Quantization (signal)
Cost function
Distributed optimization
multiagent systems (MASs)
quantization
switching graphs
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W