Average-consensus Tracking for First-order Multi-agent Systems with Systems with Quantized Data

Abstract

This paper addresses the average-consensus tracking problem of first-order multi-agent systems with constant reference signals via exchanging quantized data, where the topology is balanced and strongly connected. By transforming the quantized consensus problem into the stability problem, a necessary and sufficient condition is obtained by using matrix analysis theory to guarantee that the multi-agent systems achieve the quantized consensus, and the final consensus state is the average value of constant reference signals. Numerical simulation examples are provided to illustrate the correctness of the results.