Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication.

Xiong, Wenjun; Yu, Xinghuo; Chen, Yao; Gao, Jie · IEEE Trans Neural Netw Learn Syst · 2017

basic_science · Level V

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Abstract

This brief investigates the quantized iterative learning problem for digital networks with time-varying topologies. The information is first encoded as symbolic data and then transmitted. After the data are received, a decoder is used by the receiver to get an estimate of the sender's state. Iterative learning quantized communication is considered in the process of encoding and decoding. A sufficient condition is then presented to achieve the consensus tracking problem in a finite interval using the quantized iterative learning controllers. Finally, simulation results are given to illustrate the usefulness of the developed criterion.