title: Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication.
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.
authors.forename: Wenjun
authors.initials: W
authors.lastname: Xiong
authors.forename: Xinghuo
authors.initials: X
authors.lastname: Yu
authors.forename: Yao
authors.initials: Y
authors.lastname: Chen
authors.forename: Jie
authors.initials: J
authors.lastname: Gao